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An estimated 80% of all existing digital data isn’t neatly organized in rows and columns, but that unstructured data can unlock significant value and fuel enterprise AI applications, explains data solutions specialist Kailin Hart.
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Video transcript:
Kaitlin Hart: I’m Caitlin Hart, director of strategic partnerships at Pryon. I help build together ecosystems and technologies with our partner to build full value driven solutions for our customers.
Ken Kaplan: Now tell me how you got into this.
Kaitlin Hart: Well, I’m a data nerd. I’ve been a data nerd in all kinds of different capacities of the market for almost 20 years. I have spent a lot of time in machine learning before it was popular. And then most recently I’ve moved into unstructured data transformation at Pryo.
Ken Kaplan: And what pulled you to the other side?
Kaitlin Hart: To the other side of data? Well, so I spent so much time in structured data trying to help customers solve really critical problems there, but it’s only 20% of all data is actually structured. And so the types of problems that they would often talk to me about, data discoverability and being able to build more full applications, we actually couldn’t serve in structured data. And so when you think about 80% of all data is unstructured, it’s a huge opportunity for us to drive the needle, deliver more value. And that’s really what we’re doing here at Prime.
Ken Kaplan: And what is some of the unstructured data examples that people wanted to tap into and weren’t able to?
Kaitlin Hart: It’s PDFs, it’s Word docs, it’s websites. It could be research documents. It could be legal briefs. It could be really any … There’s about … I’m trying to think about dozens of different types of unstructured data. Even getting out to multimodal type of content like video data, audio. It’s a very large mass of data. And the difference between unstructured and structured data, structure is very organized, right? So it’s columns and rows and it’s very lightweight and size. Unstructured data because it varies so much in the type of content it actually is. So then does the size of that data. So it makes it very complex to actually transform it at scale.
Ken Kaplan: How are people coming to Priyan and what do they want and how do you help them?
Kaitlin Hart: Yeah, people who are focused on security, they’re really driven to us because we take that as a day one priority. We’ve been engineered from day one not to train on customer data to make sure that their IP is protected at scale. And that’s how we have customers like Nvidia and Leidos and some of the market leaders who take their data super seriously.
Ken Kaplan: And take us under the hood a little bit of … Describe how it works.
Kaitlin Hart: Yeah. So well, I’m a data nerd, so I don’t want to go too deep or I might lose everybody. But when you think about transformation, ETL, structured data, it’s a similar process. It’s a lot more complex because again, that data’s a lot more complex. So there’s a series of different types of models, machine learning based models that we’ve developed ourselves that handle the vectorization, the semantic understanding, query routing to question mapping. All that stuff is actually really complex, especially again at scale and to do that out of the box without training on a customer data. So we do all of that and at the very end we have the LLM, which does the smoothing and consolidation of the answer and we’re agnostic to what LM folks might use.
Ken Kaplan: And so tell me what a customer the first time that they try and they get something done that they want done, what’s their reaction and what’s the next thing they want to do?
Kaitlin Hart: Well, this is … Think about unstructured data. It’s been called content, right? As a market, we’ve separated so far out that we don’t even call it data. So when people are actually able to use it like the data that it is, it opens up in a whole new world of possibilities because with a strong data foundation, anything is possible. So they get really excited about all the different things that they could potentially build and all the ways that they might be able to evolve in the future with this. What’s next in AI and data changes every single day. We’re always on the cutting edge of something new it feels like. But I do think that data is a central really fundamental piece of this working long term. It has to be based on real data. It has to be factual. And then when you go to the next step of that at scale it has to be valuable and you can’t sacrifice too much security.
So as you kind of think about building your own solutions internally, those are the types of fundamentals you should be looking at.
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Artificial intelligence reshapes enterprise storage infrastructure, transforming passive data repositories into active platforms that power AI applications. Vishal Sinha explains how storage evolution, data consolidation, and security challenges define the new data-ready era.
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Video transcript:
Vishal Sinha: If you look at traditionally, applications and users were creating data which used to get stored in storage.
Jason Lopez: As artificial intelligence accelerates, the infrastructure we usually think of, running in the background, is undergoing its most profound transformation yet.
Vishal Sinha: Now, it’s the other way around. Now applications are using the data to build intelligence out of it and power those applications. So things have completely flipped in more recent days with storage. In the era of intersection between infrastructure and intelligence. We need infrastructure to run all the AI applications and workloads, and then you need data to power the intelligence for applications.
[Related: Data Management Strategies for the AI Era]
Vishal Sinha: Virtualization really transformed compute. Earlier, compute was tied to a server. You had to buy a server or a desktop or a laptop to get the compute. Now, virtualization made compute portable. You could move compute around as VMs or containers. And this change really paved the way for everything software defined. So that was one big transformation that happened around 2000 to 2005 time frame. After that, the cloud transformation, that has been a big one. It has really democratized infrastructure. Today, if you want to build a new app, you don’t have to build a data center to build that app. You can directly go to the cloud and build that app. Though it has created some more work, like now you have to manage the cost, you have to manage the security, you have to manage the networking. So it has added some complexity, but definitely it has made the infrastructure more accessible to everyone. And I would say the latest one is the AI. This is a brand new one. It is going to change things significantly. Even for us, the entire software stack will change to support AI. So this is still playing out. We’ll see how that evolves over time.
[Related: IT Leaders Modernize Infrastructure to Run AI]
Vishal Sinha: AI needs data and it needs a data-ready platform. And that’s where we see that in order to make a data-ready platform, you need to provide certain capabilities. First, being able to consolidate the data from different sources and bring it all together. The second piece is around making the data clean so that it can be consumed by the AI LLMs. And then the third part is providing fine-grained permissions so that LLMs learn only from the data it is supposed to. So clearly lots of transformation from a passive data repo for files, images, audio, video, to becoming a full data platform to power AI-powered applications. So that’s the big change that we are seeing.
Ken Kaplan: Talking about the new trends, what was it like before?
Vishal Sinha: Yeah, storage was a passive data repo like where customers put their files, their pictures, their videos, their audio. So it was truly looked at as a passive infrastructure mostly for storing data. And now it’s very different because now it’s an active part of an AI stack like where it needs to power all these applications with the data that it stores.
Ken Kaplan: So you see it more active.
Vishal Sinha: Absolutely, so it’s an important part of the new tech stack for AI.
Ken Kaplan: Let’s talk about customers and what you’re hearing from them. What are their needs? Do they have some new needs they’re struggling with?
Vishal Sinha: Yeah, so typically I will say two categories of customers. One who are already deep into AI. For them, the AI platform, data platform, I just talked about, that’s very important for them. But then there’s another big set of customers who are still not there. Their problem is how to manage data at scale. Like data is doubling every 18 months and now you have to support and manage that data. So that’s their number one problem. Second one that comes often is the data is now fragmented, like it’s everywhere. It’s at the edge in your phone, like it is in your applications, it’s on the desktop.
[Related: Can IT Infrastructures Meet AI Data Demands]
So how do you provide a consistent operating model to manage all this data? Then security is very important, like ransomware, probably many customers have hit ransomware. So I really want to know how the system, the storage solution itself can protect the data and not have to buy a separate solution. And also the cost, like the total cost of ownership is always a very important factor. Given the growth in data, they’re always looking for a much better, most cost-efficient way of storing their data. So these are, I would say, the four things that I hear often from the customers that we talk to.
Ken Kaplan: Do you ever hear them say, I wish I could do this or I can’t do this, but I needed to do that?
Vishal Sinha: Yeah, so that we hear a lot more around the scale part. Like so today, generally most customers have different solutions for running at the edge, in the core, in the cloud, and now they have four different solutions that they are running with. And that makes it very fragmented and they say, wish I had one console, one policy that I could apply across all of this data and that would have made their life so much simpler.
Ken Kaplan: Let’s talk about your career a little bit. What got you into technology? What was that feeling like?
Vishal Sinha: Yes, I was always fascinated by technology. I always wanted to build products that could help customers solve their problems. And for first 20 years or so, I was primarily focused on building things. And the last seven, eight years, I found it equally rewarding to take this product or products to the market and help the customer achieve the outcome that they want from these products. So this, the whole journey, which is building the products that customers love, and taking that product to the customer market, is what has been very rewarding and something that motivates me to continue with the technology space
[Related: Smart Data Management is Critical for AI Success].
Ken Kaplan: And do you remember when you first started, what were some of the big challenges or exciting things that you wanted to work on?
Vishal Sinha: Oh, absolutely. So I still remember those days when if you had to make an international phone call, it used to cost two dollars for one minute. Look at today, it’s almost free. You can use WhatsApp and make that call. So the first one for me was the whole telecommunications shift that happened between 1995 to 2000. And I was actually in the middle of it working for a startup, which was building multi-protocol label switching to really get the service provider infrastructure more seamless. And over time, it’s really brought the cost down and made everything more accessible to the end users like me and anyone else.
Ken Kaplan: And when you were working in telecom, could you imagine hybrid multi-cloud, containers, VMs living together, like all the world that we’re in now?
Vishal Sinha: So at that time, it was all monolithic. You had one infrastructure which ran everything. We didn’t have the flexibility of a VM or a container that we could move around and solve the right problems with the right form factor.
I think after the virtualization, that definitely got simplified. Now we can have a very flexible, software-defined telecom infrastructure.
Ken Kaplan: What’s the advice that you give people to motivate them?
Vishal Sinha: Learn the first principles of systems building, like don’t focus only on the API layer. Understand how systems are built so that you can appreciate and you can make better things after that. The second one would be the ability to deal with ambiguity. Things are changing very fast and we have to be able to cope with uncertain things. And look at AI, for example, things will evolve and we have to be able to make progress with what we know and be ready to adapt to newer changes. That’s very important. And third one is having endless curiosity. Things will change fast and if you’re not curious enough, we’ll not dig in and learn new things. So if we want to stay relevant, we have to be endlessly curious.
Video interview with Dartmouth College Director of IT Infrastructure Services Ty Peavey, who tells how his team chose the Nutanix Cloud Platform for managing virtual machines and container orchestration to support the university’s growing need for AI capabilities.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ty Peavey: Some people have the thought process that AI may replace what they do every day, and then there’s others that look at it to elevate what we do every day. And I kind of am in that camp. I really believe in treating AI as a tool. It’s a way to do things easier, faster, better, but at the end of the day, the human really matters.
Jason Lopez: The 2025 Enterprise Cloud Index Report found that AI applications are driving productivity, automation, and efficiency across industries. At Dartmouth College, an Ivy League Research University in Hanover, New Hampshire, that trend is playing out in real time. Ty Peavey Dartmouth’s, director of Infrastructure Services, explains how his team uses Nutanix software to modernize IT operations and support researchers who increasingly rely on AI in fields such as medicine, neuroscience, and environmental science.
[Related: Orthogonal Advantages of Cloud Native Technologies]
Ty Peavey: We have some of the best faculty in the world that really have some challenging needs. They all want access to some sort of ai. Some of ’em have small budgets, some of ’em have large budgets, and it’s really been a challenge to try to help them adopt LLMs into their research these days. And so as we look at tools like Nutanix and what we’re seeing there, we’re hoping to see that simplified somewhat and maybe commoditize and get access to LLMs on-prem early. It does take a lot of effort to help translate what they’re looking for and really appreciating the kind of resources both from people and hardware of how to deliver that. There’s also a lot of need to access AI with our vendors like Azure and with AWS. And so we try to facilitate those conversations as best as possible. We have a research group that also does infrastructure, and they’re kind of the pioneers. They’re the early adopters. They really paved the way for more of the generalists, like my team, to learn about AI.
[Related: Public Sector Adopting AI, But Serious Gaps Remain]
One of the things that we’ve done recently as more of a team building is we’ve worked with them to use some of the hardware that they’ve gotten under research to build our own infrastructure LLM. Really the goal is to try to understand the vocabulary, understand the platform and the architecture. What are the layers that go in to bringing in your own LLM? And really training it is the other thing. We have this fun approach that we want to take all of the documentation, all of the tickets we’ve processed over the years and train our LLM so that we can ask it all kinds of questions that we do every day. So it’s a lot of fun. We’ll do it for a while and we’ll take it down and then we’ll move on to the next thing.
[Related: Legacy Health Turns from Broadcom VMware to Nutanix]
We’ve traditionally had really siloed roles dedicated to storage, Linux, windows, all of these job descriptions that were very unique. And so when we got away from three-tier technology and moved into hyperconverge, we were able to break those walls down. I’m very proud of that, and I think my team has adopted it quite well. We no longer have a Windows sys(tem) admin. You may be on my team and work on Kubernetes in the morning and Nutanix in the afternoon. We brought in Kubernetes about a decade ago, really kind of some niche places that we could roll out containers. Today we have over 400 containers, over four Kubernetes clusters. Two being in Nutanix with NKP and two being in EKS. We find the adoption is great. I think we’ll always have a fair amount of full VMs, but we anticipate as our application location start supporting more and more containers. We’ll see that number grow as well. And I think what we’ve found in that is really good job satisfaction. People like what they’re doing. It is challenging at times a good challenge. And Nutanix has really helped facilitate that for us. I mean, we’ve dodged the Broadcom bullet a bit and we’re in a really good place and we’re really happy about that decision.
[Related: Search for VMware Alternatives That Meet Existing and Future Needs]
A number of things have shaken up the market and shaken up the technology. Definitely the acquisition of VMware from Broadcom is a first for me in my 30 years. Supporting infrastructure, adapting to that and trying to come out from the other side with an equal or better technology stack for our users has been really important. At the time, Nutanix and I think still does, you can buy it with AHV or you can buy it with VMware. And we literally took a vote in my staff like, okay, you’ve, you’ve seen VxRail, you’ve seen Cisco’s product, you’ve seen HP’s product, now you see Nutanix. What are we going to do? And we unanimously said, yeah, let’s go Nutanix and let’s go AHV. Let’s go all in. And it was a bit of a shock. I didn’t expect it, but it was unanimous. And the transformation had, I don’t want to say easy, but it wasn’t bad. There was a lot of similarity to concepts that we already knew. A lot of the fears were just in our head. So I would say it was an easy adoption, if you would. My team will probably hate that I said that, but it is. I think we were better for it, and I think Dartmouth is better for it.
In this video, Spark Systems Chief Operating Officer Chaitanya Peddada describes how migrating to a modern IT platform powered by Nutanix’s hyperconverged infrastructure technology boosts the FinTech firm’s high-speed foreign exchange trading capabilities.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Chaitanya Peddada: It’s an extremely competitive space, and the FX markets aren’t like the stock exchanges which start at a particular time and end at a particular time. It starts up Monday morning and goes throughout the week right up to Saturday. So maintaining that is extremely important. Customers expect to see the best prices exactly in real time, no matter where they’re sitting in the world. It’s sub-milliseconds, which means it’s microseconds.
[Related: AI Ambitions in Financial Services Tempered by IT Infrastructure Challenges]
Jason Lopez: The largest financial market in the world, the FX market, is a global decentralized network where banks, financial institutions, hedge funds, corporations, and traders exchange the world’s nearly 180 currencies at negotiated prices. Singapore-based Spark Systems is a financial technology company that creates and runs high-speed, ultra-low-latency FX trading platforms. Chaitanya Padada of Spark talked to The Forkast about its goal to double average daily trader volume.
[Related: The State of IT is Moving to Trusted Vendors]
Chaitanya Peddada: In order to do that, we have to ensure that we have all of our basics right, which means the software services on our side, the customer support on our side, and most importantly the infrastructure, because that’s where it takes a lot of lead time in order to set up the infrastructure, which we maintain. And this year we made the transition to move our entire Singapore data center onto the Nutanix stack, which allowed us to not spend money on our SAN storage devices, which would have to be renewed this year. So we used Nutanix Move to move from the legacy system to the new stack, and we also were able to add in a couple of extra servers to have a much larger stack, and we are ready to service clients or add customer servers up till the end of the year.
We are in a very tightly regulated space, so there are not just regulatory requirements but also requirements imposed by our clients, especially the banking clients, right? They have tight requirements, and these are not just security requirements but even data storage requirements. And that’s the reason why we host our own hardware. We don’t host customer data in the cloud. And because we have to maintain our own hardware stack, it’s very important that we, apart from our own software, which is Java-based technology, we partner with the right partners right from the OS level to the VM level, as well as the entire infrastructure stack.
[Related: How Ivy League Dartmouth College Moved to a Future-Ready IT Platform]
We host our own software in data centers, which are maintained by us for the customer, which means as soon as the client signs up, within days, if not weeks, they want to start up and start trading right away, right? So, having the right platforms in place, being at the right locations, which are the FX hubs around the world, is extremely important. Apart from that, latency plays a very key factor. We need to be in the sub-millisecond space, right? And those are the sort of benchmarks we use while testing any third-party product. So, whether it’s the OS or whether it’s the VM solution that Nutanix provides, these are some of the basic test cases that we run through, apart from load testing and other FX parameters.
[Related: AI’s Rising Tide Confounds IT Decision Makers]
We also consume a lot of data, and per day, there’s an immense amount of market data that’s going through our systems, as well as the client’s proprietary trading data, orders and trade data. All of this data has huge potential in order to leverage for building TCA systems or AI-enabled TCA systems, right? What we are looking at doing at the moment is to use this data in order to give our customers trading signals before time. We also have a library of algorithms which have predictive capabilities. Now, when we get into the predictive space, it’s very important for vendors like us to be able to assist our clients in order to provide new strategies for them, because in terms of predictive AI or algos, we need to really partner with the right customers. The traders are the people who come up with the ideas, but we need to be ready to implement those ideas as soon as possible. So this also allows us to leverage on the cloud technology, which we haven’t done so far, but even over there, I think Nutanix is probably the right partner for us to have on-prem services as well as cloud services using the same technology stack.
[Related: Legacy Health Turns from Broadcom VMware to Nutanix]
This was the year we transitioned onto Nutanix in a big way, right? We had initially started off in 2022, experimenting in our London data center, which is a smaller footprint compared to Singapore and Tokyo for us. And this year, we took the decision to transform the entire Singapore data center onto a Nutanix HCI stack. The most interesting thing about this transformation was, as I told you, we have only a small window to do a migration per week. So it’s one and a half days per week. And over a period of six weekends, our team managed to transform the entire stack onto a Nutanix HCI stack. We’ve gone issue-free so far, which is a first, probably in my career. We haven’t had any migration issues because there was also a lot of support from not just from Nutanix, but also from Nutanix partners to practice and prep for this migration process.
In this video interview, Nutanix CEO Rajiv Ramaswami discusses 2025 IT trends, including migration to trusted vendors, the rapid evolution of AI technologies, and mastering the long-term state of hybrid multicloud environments at scale.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Rajiv Ramaswami: Now it’s time for us to look at the most modern or modern applications, AI. As the year progressed, we saw AI, generative AI evolving at a rapid clip. Here we’ve expanded from just doing simple inferencing to providing you a full set of age agent frameworks and models.
Jason Lopez: At the .Next 2025 conference in Washington DC, The Forecast caught up with CEO Rajiv Ramaswami after his keynote to briefly chat about technology and market trends.
Rajiv Ramaswami: If you’re a customer, I think everybody wants to know about what’s going on now? What are the trends? What’s Nutanix doing? How can we help these days? Of course, top of mind topics for everybody is what’s their future end state when it comes to a vendor that they can partner with for the long haul as they migrate away from their current solutions. So that’s a very top of mind topic for everybody. And then we quickly get into a discussion of trends and where things are going for the future and how we can help them. For the longest time, VMware was the established infrastructure vendor for many of these companies. They’ve had 20-year-plus relationships with them, and I was in Japan, and I think there was a growing awareness two years ago. They weren’t particularly concerned. Last year, they started getting concerned.
[Related: Nutanix CEO Stokes Surge in IT Ecosystem Partnerships]
This year they’re actually moving. For example, here at the show, we had Toshiba talk about how they’re planning the migration away from Vme Nutanix. And you’re seeing that, right? I mean, some of this is also from certain cultures. There’s a lot of faith and trust built up over the years, and they want to work with somebody they can trust. So we are seeing a lot of that certainly happen over the last year. We’ve gone even in terms of what we’ve been able to bring to market and made tremendous progress. And it’s still not mature. There’s a lot going on every day, and I don’t know what it’s going to be. It was regular AI, generative AI, then agentic AI, I don’t know what’s coming next. Reasoning models came in recently. So this is rapidly evolving. I think hybrid cloud is pretty much established at this point.
[Related: Growing a Multibillion Dollar Data Security Company in the AI Era]
Everybody realizes they’re going to be operating in a multicloud environment with some workloads running in the data center, some at edges, some in the public cloud. So that model is very well established. And almost every customer I talk to has made these comparisons. They’ve looked at the cost of running in a public cloud, whether on-prem, they’ve looked at the security issues, all of those things, and they’ve decided that they’ll figure out some things that were done in the public cloud, some and on-prem. So that’s well established at this point. Now it’s a matter of more executing on that at scale.
In this video, Cohesity CEO Sanjay Poonan tells The Forecast about advice he got from Nvidia CEO Jensen Huang that led him on a journey to build a multi-billion-dollar, AI-powered data security company.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Video transcript:
Transcript:
Jason Lopez: Cohesity is a cybersecurity company led by Sanjay Poonan. He told The Forecast how he came to Cohesity on advice from Nvidia, CEO, Jensen Huang.
Sanjay Poonan: He said something that really rang with me, which is, you’ve done the big company stuff, 20 billion, 10 to 20 billion at SAP, six to 12 billion at VMware. You haven’t taken a small company and made it big, so you should try that. And for me it was a little bit of like, can we take something that was small and make it an iconic company? SAP and VMware were already iconic when I got there, and we of course made it even better. That’s what I saw as the opportunity of Cohesity. It was a world-class company. It was about a $300-million company when I joined, with fantastic technology and an incredible founder. In fact, many of the customers told me it was the best tech they’d seen since VMware, so there was no tech risk there. We needed to scale the go-to market to get it to be a multi-billion and the leader in the space. We were number seven. So I saw a tremendous opportunity at the center of this notion of data security because of ransomware attacks and all the pressure on data to create a leader, and that’s what we’re off doing today.
[Related: Composable Data Centers That Power Enterprise AI]
The entire product is based on notions of machine learning and AI. Before generative AI, the founders were Google folks who built a lot of key capabilities of ML and AI into the product. A lot of these cybersecurity data detection of entropy and anomaly detection are AI algorithms. So that was from the get go, built to be a key part to the proposition of the product driven by Generative AI. We started working with Nvidia. Nvidia made one investment in this space. They looked at all the tech companies and picked us to do an equity investment because we cracked an important problem of how you could use retrieval augmented generation, also known as RAG, directly on secondary and backup data. We invented that. We in fact patented the idea. Nvidia loved it, and we were featured at GTC in both 2024 and 2025. Jenssen’s words were Cohesity backs up the world’s data and they’re building their RAG application called Cohesity Gaia on top of the Nvidia platform. We’re working with them and Google and Microsoft and Amazon who are also those four companies, I think pioneers in what the world of AI is going to look like. You can imagine a problem like this where hundreds of millions of PDF documents sit in our backup. You can easily query that, summarize it, get insights into sort of flying a flashing of torch light right into your data. That is going to be a tremendous advantage to not just keeping the data secure, but also getting insights into it.
[Related: Raising Ransomware Resistance]
Today, the number one problem is data resilience. People’s data is under attack from nation state actors, cyber criminals, and we work with 13,000 customers. The largest of the largest 85% of the Fortune 100, 70% of Global 500, and it’s the name brand and financial services, tech, telco, healthcare, you name it. The public sector are our customers. So their number one priority is keeping their data safe from ransomware attacks. And we’ve designed something that’s got the fastest cyber recovery and enormous amount of advanced security. Now, what they want to be able to do is, let’s take that a step further and using AI tools, get insight into that data, and we have a very good sort of two-pronged engine approach to how we approach our company’s ambitions. Number one, we innovate to be the best product in the industry, and that’s been always the pride of Cohesity, I hope, and we will continue to do that.
[Related: Protecting Against Ransomware at the Data Level]
Number two, we obsess about our customers. We get an anchor customer, particular vertical. We study their needs, we drive what they need. And then every customer, that vertical becomes automatically like that anchor customer. You pick the largest bank, make ’em successful, pick the public sector agency and the federal, the biggest telco, the biggest tech company, the biggest hospital. And when you do that, you can repeat that. That’s the playbook I learned at SAP and at VMware and now you get to apply it at a smaller company. We’ve gone from 300 million now with this major acquisition we’ve done at Veritas. We’re now on the path to being a 2 billion company and the biggest space, number one, but the enormous amount of innovation and we’re pouring a lot of engineering including into our exciting new work we’re doing on Nutanix, which I’m very excited about.
In a video interview, Nutanix’s Lee Caswell tells how IT customers need to meet changing needs as they embrace AI capabilities.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Video transcript:
Jason Lopez: In between press briefings, Lee Caswell tells The Forecast about some of the biggest IT trends, challenges and innovations explored at the 2025 .NEXT event in Washington, D.C.
Lee Caswell: We’re really interested in that customers get value out of AI. And this is one of the things where a lot of customers today are a little bit stalled trying to figure out, well, hey, I don’t want to spend too much. I don’t want to spend on the wrong thing. How do I get started, right? And how do I also make sure that the AI that I bring to market has the same enterprise level resilience, the same day-to-day operations, the same security and privacy that I bring for all of my traditional apps. How do I make AI just the next enterprise app? And so we’re helping customers basically with choice. So the idea is, what are the fast-changing parts? GPUs are changing pretty fast, so we’re going to give you the access to the GPUs or CPUs with acceleration, for example. LLMs are changing fast. Last year, we gave you almost an app store, if you will, into LLMs from our partners Hugging Face and NVIDIA. This year, now we’re supporting agentic workloads, which is an iterative cycle where it’s not enough to just get an answer. Now, I want to iterate on that answer and give you more relevant results, maybe through guardrails, maybe through re-ranking, maybe through embedding. And so we’re giving you all of those access points so you can get started today and have a production-ready enterprise experience.
Jason Lopez: Caswell talked about how enterprises are onboarding AI and managing data privacy.
Lee Caswell: Well, I think first off, we’re helping customers understand the fast pace of the AI market. So most customers today realize that large language models, or they don’t have to be that large, it could be SLMs as well, will be developed either in the public cloud or by some of the largest customers because of the incredible capital expense of training a model. So now customers are looking and saying, well, all right, I’m probably going to get access to a model, but how do I make sure now that my data remains private? It could be, for example, that your model becomes more sensitive than the data itself because the model now starts showing you’re inferencing your ideas, your insights into the data that you have. And so that idea of saying, it’s going to be important to access large language models and change them out. I want to have access to that. We provide that capability. And then I also want to have this ability to go and have more responsible results and be able to go and manage the infrastructure so that it’s actually an enterprise-level experience. That’s how we’re helping customers basically get started with our Nutanix AI, enterprise AI products.
Jason Lopez: He tells how Nutanix software helps enterprises build and run a future-ready IT operation.
Lee Caswell: Yeah, it turns out, right, you know, the quality of our engineering has been really something that’s just been delightful to see as we’re bringing all the elements necessary to bring AI into the enterprise. And so that could be, for example, like the storage, right? You have to ingest that data. You’ve got to run the model. You’ve got to archive the data. How do you do all of that together? We provide an offer for that. In addition, by the way, many of these models are containerized. And so as you bring containers and Kubernetes, for many customers, these are relatively new concepts into the on-prem world. How do you leverage what you know already today? And here we acquired a company called D2IQ, and we have a terrific set of engineering leadership resources there who are now taking that, what was a production shipping product, and now adding Nutanix data services. It’s an incredibly compelling offer and part of the complement of products you need to make AI successful.
In this video interview, Liqid CTO Sumit Puri explains why dynamic IT infrastructure that taps into pools of GPUs and scale-up memory can quickly and efficiently run virtual machines, containers and AI workloads on-premises and at the edge.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Jason Lopez: Liquid is a software-defined infrastructure company that enables more efficient use of high-cost, high-power resources like GPUs in data centers. Instead of installing GPUs directly into each server, Liquid creates centralized GPU pools and dynamically allocates them to servers based on workload demands. This composable approach maximizes GPU utilization, reducing waste, and improving performance.
Sumit Puri: We saw this vision of GPUs being important in the data center many years back, and then all of a sudden this thing called ChatGPT burst upon the scene and made this front and center in everyone’s mind. And so we’ve been focusing on pooling and sharing these resources for a long time, and now all of a sudden AI is forcing itself into the mainstream, especially in the areas that we focus on, things like the enterprise, and now all of it’s coming kind of market for us in a very, very good way. And so we ended up building the product, and the market ended up coming our way.
[Related: Nutanix CEO Stokes Surge in IT Ecosystem Partnerships]
Jason Lopez: Liquid addresses the growing demand for AI inference workloads, especially for enterprises that want to keep their data on-prem instead of moving to the cloud.
Sumit Puri: What Liquid focuses on is very much on building power-efficient, cost-efficient solutions for inference. And it’s interesting to think whether it’s on-prem or it’s in the cloud, a lot of the data, which is what a lot of this AI is driven on, it lives on-prem. Eighty-three percent of the data is actually on-prem. And so one of two things must happen. We either must move the data into the cloud, or we must bring the GPUs on-prem. We think there’s a lot of customers who are not willing to wholesale move their data to public cloud, and so therefore we want to build efficient solutions to allow them to process their data on-prem.
[Related: Swarms of AI Agents Powering Businesses and Daily Life]
Jason Lopez: There are advantages of pooling and sharing GPUs instead of deploying them directly in each server.
Sumit Puri: There’s three primary benefits why somebody takes this journey of pooling and sharing the GPUs. One is around performance. I need that server to have a lot of GPUs because I need it to run very fast. We’re not limited by two or four or eight in a box. We can compose 30 GPUs to a server and give you the fastest servers on the planet. That’s one reason. The other is cost. If I have to deploy 30 GPUs, do I want to buy four servers, put eight GPUs in every server, buy a bunch of networking? Or do I want to buy a single server, deploy 30 GPUs, reduce my cost, reduce my power, and have a more efficient way of deploying these resources? And the third reason is agility. It’s very hard to predict, will my workload need one? Will it need two? Will it need four? Will it need eight? Do I use an H100? Do I use an A100? Do I use an L40S? There’s too many choices, and so we try to take the guesswork out of it. Let’s put a centralized pool of whatever device type we need and pick the right tool for the right job at the right time.
[Related: Search for VMware Alternatives That Meet Existing and Future Needs]
Jason Lopez: The AI infrastructure landscape is evolving from training to inference, especially for companies that are not building foundational models.
Sumit Puri: The first chapter of this entire AI journey was very much focused on training and building these foundational models. And the way that we see that going forward, there’s probably only going to be 10 companies on the planet that can afford to build these massively large 100,000, 200,000 GPU clusters to build the foundational model. The other 100,000 customers that are out there, they’re going to take these models, open-source models like Llama, they’re going to bring them on-prem, they’re going to fine-tune that model, they’re going to do RAG, they’re going to do inference, and that part of the journey now is just starting. We think by the end of the decade, inference actually is going to be a larger piece of the overall AI pie than is something like the training portion of it.
Jason Lopez: Liquid makes AI inference more accessible and efficient for enterprises, especially with Kubernetes and model deployments.
Sumit Puri: So NVIDIA has a big push for something called NIMS, NVIDIA Inference Microservices, which is basically a container. And what they’ve said, it’s very, very difficult for people to get all the layers of the stack perfectly right to deploy these models, and so we’re going to containerize these models, and that is the way that enterprises are going to go off and deploy this. We have a plugin for our solution where what we do is we suck the container in, we probe the container, we figure out what type of GPU and the quantity of GPU in the backend, and we connect that GPU resource to the specific server in the Kubernetes cluster, then we deploy that container on that machine, so you have this perfect matching of hardware to container, and we automated the entire process. We’re at a point now where we have one-click deployment of inference. You say, give me Llama7b go, and we will automate the entire process on the backend, and within two minutes, give you a model that you can speak with. When you’re done with it, you hit the delete button, we’ll rip those GPUs off, put them back into a centralized pool so that next container, that next model that you’re looking to deploy has resources to use. We think that’s how you get there, is you have to make it easy for enterprises to deploy these things. They don’t have the large armies of data scientists to do this on their own, so the more that we can automate this, the easier it is for those companies to consume, the more that we can make it more efficient, and the way that we think about efficiency is tokens per dollar and tokens per watt, because that’s what the enterprises are limited by, they’re limited by power and money. If you’re a hyperscaler and you have unlimited power, unlimited money, we can’t help you. But if you’re an enterprise, that is the metric you need to think about. Tokens per dollar, tokens per watt, automation, ease of use, those are the things that are needed to get AI to scale.
[Related: Bracing Data Centers for Wave of AI Workloads]
Jason Lopez: The company supports virtualization and dynamic GPU allocation in enterprise environments.
Sumit Puri: We are a platform for a variety of different applications. One of the applications we are very well suited for is virtualization. If we think about VMs for a second, it’s very difficult for enterprises to predict which VM they’re going to deploy at what time and what resources that VM might need. We deploy infrastructure for three to five years and making that long-term prediction is very difficult. We’ve partnered with Nutanix where we can put a centralized pool of GPUs inside of a Nutanix cluster and depending on the requirements of a specific VM, we can match the GPUs on the fly dynamically, hot-plugging GPUs into servers to meet the requirements of the VMs on Nutanix.
In this video interview, Harmail Chatha, senior director of cloud computing operations at Nutanix, describes the growing challenges of managing data centers as business demands for enterprise AI applications climb.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Harmail Chatha: You have GPU clouds available like AWS Google and Azure, but they’re kind of the true ias PaaS platforms. Now you actually have clouds that offer you bare metal with GPUs. And what’s happening across the industry is there’s a lot of companies that are deploying these in traditional data centers and taking up a lot of the power and space as well, because we’ve always had this concept of hyperdepth racks where we optimize for vertical growth versus horizontal growth. But what’s happening within the data centers now is a lot of horizontal growth because of not enough power available, not enough infrastructure available to support the power needs of GPU environments. And obviously cooling isn’t there as well. So I don’t think AI is necessarily pushing the limits within data centers yet, but it’s going to very, very soon if data centers don’t start to adapt to new technologies, new cooling infrastructure, new power densities that are required as well. So I think we’re going to start seeing the limitations within data centers, but I don’t believe it’s there yet. But as more and more consumption goes in and customers identify workloads that they’re going to be running with ai, I definitely see it hitting a limit.
[Related: AI Lifecycle’s Impact on IT Infrastructure]
They’re at the heart of the power problem in the data centers right now as well, right? These newer generations of CPUs with GPUs are consuming anywhere from 30 to 50% more power within the servers. Hence, the industry is really taxed from a power consumption perspective. Whereas you can deploy a full rack of the older gear now you can only deploy half a rack. So how do you solve that problem? So now, whereas we historically have been 17.3 kilowatts per rack, fully maximizing the rack. Now our new design, it’s going to be 34 plus kilowatts per rack with liquid cooling to the rack and ultimately getting to the chip as well. So we’re really at the onset of designing our data center of the future also, because what’s legacy is not going to work any longer. It’s going to be super inefficient. Cooling challenges within the data center. Air cooling is not going to be enough with this new AI technology going in and the consumption of power within the GPUs as well.
[Related: Report Shows Enterprise AI Driving Big Investment Burst in Cloud Services]
Companies have to start really honing in or zooming into their environments. Not so much holistically at a data center level, but what does a workload look like and how do you measure the emissions of that workload in itself? Right? And we’re just kind of touching the surface on scope one, scope two, how do you really measure scope three, which is the most challenging one? It’s basically considered everything else that’s not direct emissions, indirect emissions, but scope three being all encompassing. How do you get to embody emissions as well as an industry? We’re not there yet embodied emissions of a server. So we’re talking about VMs to workloads, but embodied emissions means what’s that single little cable within the system, the server itself, and how do you measure the emissions of that? There’s thousands, hundreds of thousands of parts that go into a server. How do suppliers measure the transportation cost and the development cost of those components as well? So really it’s all about zooming in right now, right? As we continue to mature in this space, there’s a lot of effort that’s going to go into measuring, and there’s so many companies, new startups coming out that are starting to just touch the surface of how do you measure emissions in itself?
[Related: Guiding Enterprise IT Hardware Buyers into the AI Future]
ROI has a lot of interest from companies wanting to learn and understand sustainability. It’s no longer like a hypothetical topic or a conversation, but kind of roll your sleeves up and you have to take the step, take the initiative to first kind of educate yourself, what is it that you need to do, understand the different scopes there are, and really start to measure what your footprint looks like. So what I’m seeing is, of course a lot of interest in the industry, but I think what’s happening with some of the, for example, us who’ve been on this journey for the last three plus years now, or some of the more mature companies that are been measuring their carbon footprint, we’ve been doing this at a very holistic data center level, at a building level, and then kind of have gone down to a customer’s environment level.
[Related: Get a Grip on Data Storage in Quest for Enterprise AI]
So we have data halls, we have cages, so we’re able to measure our footprint there. But now, as we announced just earlier today in the opening keynote is worse within (Nutanix) Prism Central application, we can measure the electrical consumption of a node, and ultimately you can get to a cluster. So we’re going a step deeper versus just being holistic at a data center level now. So this is a good step in the right direction, but where we ultimately need to go and continue to do more work is you got to get to the VM level. Once you can measure the vm, then you got to get to the workload level, and that’s when you’re going to be able to make smart and intelligent decisions on what a workload consumption looks like, correlate that back to the emission factor, and then intelligently you’re able to move those applications around to more sustainable data centers that might have lower P use more renewable energy as well. So I think that’s the journey we’re on. I’m glad we’re measuring at the node level, but ultimately we got to get to that VM and application level as well.
In this video interview, the inherent group President Pierre Jean Beylier describes the state of digital transformation across Europe, including data sovereignty and regulations, public and hybrid cloud, IT resiliency and flexibility, and sustainable energy strategies.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Pierre-Jean Beylier: Today we’re a French player with a small business starting in Belgium, but we’re really a French player. So we’re very focused on what’s happening in France in terms of cybersecurity. And already it’s complex, but we’re dealing with one big government agency that deals with cybersecurity Europe. Each country has its cybersecurity agency. Each country will want you to be certified with their local agency if you want to work with certain government agencies and so on so forth. So it’s a lot of work. It’s additional costs in many cases because you don’t have a certification that allows you to work smoothly around all the European countries. Now, there are some rules in place that give you equivalence. So if you have this level in France, then it works in Germany with some countries, but not with all. So it is very complex in France compared to the rest of Europe. In Northern Europe, we are late in the move to cloud, whether it’s private cloud or public cloud. So we are catching up on that, and we’re also late securing our data and our networks and our infrastructure. So small, medium sized companies have a lot of work to do on that front. In France, 60% of the small companies that get attacked get out of business within 24 months after the attack if the attack was successful.
[Related: Managing Enterprise AI Sprawl]
So there’s more and more education around those risks, and we see that evolving. Obviously, that creates a lot of opportunities for us helping our customers with securing their data, their network, and with helping them define what is the best setup of the infrastructure. Are there things that they want to keep on-prem and it makes sense to keep on-prem? What do they put in a private cloud? Is there a need for them to go to the public cloud? We advise them on that and then deploy it with them and manage it for them. Larger companies can co-manage or let them manage themselves if they want, but smaller companies in general, it’s a fully managed service.
[Related: IT Analysts Discuss the Search for VMware Alternatives After Broadcom Acquisition]
They need a lot of things. They need more reliable, resilient connectivity, and higher speeds. With that comes the need for more security. They have a lot of their systems. If you look at the French market, we are late in terms of moving to the cloud. So they have a lot of systems on-prem, and they’re asking themselves, does it make sense? Wouldn’t I be more secure if I put my systems in an environment that has been built for that and managed by people whose job day after day it is to do that. So typical needs of companies whose business model is evolving towards more and more e-commerce digitalization of all their processes, they’re coming to us. So we are really focused on mid-market companies from 50 employees to 5,000. And then we have also larger companies that come to us when they’re fed up with the big telcos and the lack of agility and response they’re getting.
[Related: 4 Trends Defining the Future of Enterprise AI]
At the beginning of the process, the customer was not sure of what he wanted. It often happens or they think they know what they want. And when you start going through different scenarios with them, no, actually this sounds good. That’s not what we had planned for, but this sounds good. And we wanted a technology that allows us for maximum flexibility because we were not sure where we would land. Maybe some servers would stay on-prem in some stores in the bigger stores, some private cloud they had in mind to have some in frying the public cloud as well. So we wanted the technology that allows us to manage that very smoothly wherever the infrastructure is located. So Nutanix came on top of the list to achieve that. And then we also wanted to go into a hyperconverged solution. So that was a key element in the decision.
[Related: Enterprise IT Teams Jump Into AIOps]
We have 14 data centers in France, seven that we own. And we are looking at a, what can we do better? So what’s interesting in our industry is that 80% of the carbon consumption, carbon footprint comes from terminals. Data centers are very small for us because in France, most of our energy comes from nuclear energy and it’s a green energy, with very low carbon emissions. So energy consumption is not a big issue. Water consumption is, and we’ve put in place cooling systems in our data centers that don’t use water. So we’re quite on the forefront of that. And then in terms of terminals, last year we collected from our customers a bit over 14,000 routers and all kinds of different equipment. And it’s roughly one third we refurbish and reutilize. One third we sell for other people to utilize them, and one third we recycle, destroy, recycle properly. That’s a big objective for us, and we think we are ahead of a lot of our competitors in that 80% of our carbon footprint comes from the equipment at customers, premises, routers for fiberlink, et cetera, et cetera. So that’s a big area of focus for us.
In this video interview with The Forecast, Simon Robinson, principal analyst at Enterprise Strategy Group, discusses the complexities of managing data in cloud and hybrid multicloud environments, a challenge that is growing more acute with the rise of enterprise AI applications and data.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript (edited):
Simon Robinson: We talk a lot about storage, but the important part is the data. That’s the stuff we care about. Managing data according to policy in a cloud environment or multicloud environment is much more complex because it’s much bigger and there are strict rules about what data can go away. So enabling that longer term I think is a really important problem that the industry is focused on addressing. One of the big challenges in the future is going to be: how do we get enough and the right sort of data into those models? So that has a couple of dimensions. One dimension is if I’m building massive GPU clusters, I want to be able to feed data very, very quickly. So I need highly parallelized capabilities on the storage side to keep those GPUs saturated. So that requires some very particular, very high performance storage capabilities.
[Related: 4 Trends Defining the Future of Enterprise AI]
So that is definitely happening. More broadly, I think for the typical enterprise, they’re now at the point where they’re thinking, okay, so we’re going to maybe have some models, build some models ourselves. We’re going to be thinking about what these applications may be. They may be running at the edge. Where is the data going to be for those applications and how do we get data into those models? Storage today is very, very siloed and it’s trapped into different locations. It’s in different flavors of storage. You’ve got sand, you’ve got nasa, you’ve got object storage, you’ve got storage in the cloud, you’ve got storage at the edge, you’ve got storage in multiple data centers. What is the right data that I need? Right? Organizations struggle with visibility into their data. They always have, and there’s always been kind of, well, what is the benefit of solving that problem? And I think AI could be a major enabler in persuading organizations to finally solve that problem. If they don’t have visibility into their data, they’re not able to optimize their AI environments. So I think that’s where we see a real opportunity in some really interesting things going on in the industry in terms of innovation there.
[Related: Enterprise IT Teams Jump Into AIOps]
When we are talking about billions of parameters about generating insight from stuff that wasn’t created by humans, that can only at scale, that’s accessible globally, that can only be done in the cloud and very likely that will be the case for a great many years. Again, the question is how do we take some of those learnings and apply that to our on-premises environments? And it could be that actually we don’t need a large language model. We actually might need many small language models. So how do we build an environment that is able to ingest elements of the public cloud, of the model that’s in the public cloud and apply it for our own data? I think that’s the key. The challenge for organizations, they can become sort of paralyzed by, I dunno where to start. The important point is just start. Just do something. Because the sooner you get going, the faster you’re going to learn. The faster you’re going to perhaps fail or make some mistakes and then learn again and do it again. And the faster you’re going to get to a kind of maturity in terms of delivering some real capability to the business.
Analyst Jean S. Bozman, president of Cloud Architect Advisors, explains how evolving traditional IT to hybrid cloud and enterprise AI forces IT teams them to manage complexity, acquire new skills and manage a myriad of costs.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Jean Bozman: It’s amazingly well accepted. It’s not controversial. Hybrid multi-cloud. You cannot almost go to a tech person that wouldn’t agree with that statement. There’ve been different terms. There was hybrid cloud than they said because there were multi-cloud. It’s hybrid, multicloud. I think it’s an admission. That data is everywhere. And so I’ve distributed data and that was fed by the fact that we had the hyperscaler guys. Some people call ’em CSPs hyperscale, where the big guys know who they are. And the data is not just sitting in one monolithic place, it’s sitting here, there, and everywhere. And even the people who used to think that it would just be on one or two systems, it is not that way anymore. Anyway, so it is a fact of life, but the question is how do you live with it? It’s that way. How do you live with it? How do you get the two things that have been running in parallel, which is distributed, we know that’s happening, but also the traditional enterprise stuff. How do you get those things to sync up and work together? And I think a lot of what we’ve heard here is about how do you get those enterprise capabilities, manageability, all the things we’ve always had to have and how do you make that happen in a world that may otherwise seem chaotic?
[Related: Stepping Stones to Cloud]
This is a challenge for the enterprise where people say, Hey, that’s not how I’ve been doing things for 20 years, or Hey, I didn’t learn how to do this, or I need these new skills. So there is a skills challenge and there’s also a cost challenge because we know what we were doing and the thing we are doing seems additive, but now we really want to kind of embrace everything and do it all at the same time. People were living separately, right? You had the people who always advocated for distributed and lots of little databases here and there, and that was cool. Especially forgive me for my sins, but the open source movement. So fun. But it’s okay to have fun, but you also need to run a company. So you need to put those business best practices. I want to say the best practices to work and that’s what could be a chaotic environment, but should be a smoothly running, unified environment.
[Related: Managing Enterprise AI Sprawl]
A lot of the AI stuff comes out of the same kind of people that have been doing the open work, but it’s applying, I think a new rigor to it. And also there is the demographic piece. I’m absolutely going to talk about that. Where traditional IT has been running for, what, 40 years or something like that. A lot of these things are a sudden shock to systems that have been reliably in place when you talk about let’s do backups, let’s do security. It was done a certain way. All that’s having to be adjusted, I think, and that’s clear to people.
[Related: 4 Trends Defining the Future of Enterprise AI]
AI was kind of, I don’t know, it was almost a shock to people. All of a sudden, it’s in your face now what do I do? I’ve got lots of data. I’ve got these models. I know other people here. We’re talking about the models and where they are and who’s going to do it. And there’s a very big difference in training the models, which takes up a lot of CPUs, GPUs, software. But there’s also the inference stuff, and that can be customized to your business. Healthcare, retail, finance, it’s customized. And also it’s easier to get your arms around it than trying to take in everything that was ever written just to write a haiku. That’s not an enterprise thing by itself. It can fit into the enterprise. People writing things, people being creative, but it’s not enough. There’s a lot of rigor that has to come around that controls guideposts and all of that.
[Related: Enterprise IT Teams Jump Into AIOps]
All the things we were always careful for all these years. So AI is tremendously exciting to people. By the way, I’ll give you my controversial statements, which is Tech GT is great, but AI is much, much bigger. We had ai. AI was in the background doing data management, finding financial fraud, looking at medical records. That wasn’t sexy enough. But now it is more sexy. It’s in my office, I can see, oh ai, that’s great, but it’s not enough. It has to blend in. Great tool, wonderful to learn about and use. Very useful, but it needs to mix in with everything else that’s been developing over the last 10 years.
In a video interview with The Forecast, DataRobot CEO Debanjan Saha discusses the need for finding value and building confidence to achieve enterprise AI success.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript (edited for readability):
Debanjan Seha: I was in a Morgan Stanley investment conference in San Francisco, and of course every investor quizzed me about ChatGPT and GeneAI. On the way back, the Uber driver quizzed me for half an hour about ChatGPT. And I realized that AI has arrived. But there are limitations to what some of these models can do. So what are those models doing? I mean, we have trained them on a really, really vast amount of publicly available data and sometimes on proprietary data. And once you run out of those data sources, there is a limit to how those models are going to get better and better over time. Of course, there will probably be better and better models, but I don’t think it can get incrementally better for an infinite amount of time. That’s not going to happen. Now, we are already pretty much fed all of these models with all the written text data that humankind has produced over the last hundreds of years.
So there is not too much text data left that you can feed these models to. What is left, by the way, is a lot of audio and video data, and that data is much easier to collect. And there are a lot of things which you don’t write down. For example, when you lift a cup from a table, nobody writes it down, but there are a lot of videos which show how we go and grab the cup and pick it up from the table. So those are the kind of data sources which are now going to make these models more and more intelligent. But I do think there is a limit to which you can feed them information and make them better.
[Related: Building a GenAI App to Improve Customer Support]
I see two gaps that we are really focused on. One I call the value gap. People are spending a lot of money training models, but ultimately those models have to solve some business problems. They have to make things more productive or predict things more accurately or whatever it is. Business value has to justify the investment that’s going in there. And it’s not easy with AI to create that business value. And one of the things DataRobot does very well is to connect business problems with AI to solve them. So that is the first problem that we are focused on. The second thing, which I also see with any new technology, is you need to build trust. AI is kind of in that phase and there is a confidence gap. There are a lot of people who are creating, especially with GenAI, a lot of prototypes.
[Related: Enterprise IT Teams Jump Into AIOps]
It’s not difficult to put together a chatbot which answers some questions. It’s hard to guarantee that they’re going to answer the question correctly, that it’s not going to create any misleading answers or create harm in more serious cases. And there are a whole lot of things we need to do in terms of managing the risk and creating a framework where you can define and associate risk with various different types of use cases and handle them with the right level of mitigation, compliance, testing and validation, et cetera. So those are the two things which are very, very important for wide scale adoption of AI.
[Related: Role of CIO Expands with Enterprise AI]
For example, the value gap. So the most interesting and most challenging thing about the value gap is that you need people who understand the business and the business challenges that they’re trying to solve. And you need people who understand AI and how those two things can be put together in order to solve a business problem. So we do ideation workshops with our customers. We sit down with customers and their people who understand the business and work with them to figure out their business challenges and what use cases they have. And then our team works with them to figure out how to address those use cases. Do they have all the ingredients…the right datasets? Do we know what business processes need to be updated if we insert AI into that? Those are the things we do first. Then people can use our platform either themselves or we can help them as co-pilots. And we sometimes have our other partners, the service provider partners, to work with them to solve those business problems and actually build those models, build those applications, and then put them in production.
Pretty much every industry you go to, there are enough problems that AI can help solve. And pretty much every business line you go to, there are enough problems that AI can help solve. But bringing AI to business, that’s still not easy for everybody because you need to speak both AI and the business language. And it’s very difficult to find people who speak both. So you have to have two in a box and solve those problems.
In this video interview, Taylor Linton of Hugging Face explains the thought process many IT decision makers go through as they select open source and proprietary AI software to run their businesses.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Taylor Linton: With open source. There’s really two categories of benefits with using open source models, and it’s really broken down into strategic and tactical reasons. To start off on the strategic side, it really comes down to ownership of an ip. Meta has done a great job commoditizing the models and other open source model trainers. So the fact that these companies can take their proprietary data and customize these models, that’s a great opportunity to be really a competitive differentiator from their competition, having their own models that they own that are trained on their proprietary data. Another big reason on the strategic side, it really comes down to cost. And what I mean by that is sometimes your incentives aren’t aligned when you’re using a proprietary model because they charge based off of input data and output data. So the more value you get, the more charged. And so the fact that you can build an open source model and you’re only paying for the commodity hardware, your goal is to get as much usage as possible, whether it’s getting it in front of more customers or getting more AI assistance used from your employees, not only are you able to train your own model and keep it in your environment, but there’s a lot of security benefits.
[Related: Role of Open Source in AI]
Customer trust is huge. Responsible AI is huge, and the fact that you can feel confident that you are sending your data to a model in your environment, it’s a great opportunity to really de-risk it. Another part on the tactical side comes down to latency. If you don’t own the model and you can’t control the infrastructure that it’s running on, there’s many times when requests might take four or five seconds to get a response. So when you have ownership of the open source model, it allows you to make sure that you get that ultra low latency to support your requirements.
So that’s one of the things that people really appreciate with open source models because you can download the model and run it in any environment you want to. So it could be their preferred cloud provider, it could be on-prem, it could be in their Nutanix environment too. But really it comes down to where do they want to run it? Where’s their preferred environment? We often suggest our customers to go grab their own proprietary model, test out the use case, see how it performs. It offers a good benchmark to know roughly where AI is to be able to support the project from an accuracy standpoint. Now, once that POC has been built, that’s where we work with them to say, okay, how should we build this with an open source approach? Now, open source AI is not a silver bullet. There’s really trade-offs too. So when you start looking at cost, these models, they scale very well from a cost standpoint, open source models, and the reasoning is because you’re paying for the commodity hardware that it’s running on.
[Related: Enterprise IT Teams Jump Into AIOps]
The downside to that is if you don’t have a large volume use case, it could require some expensive compute to host that model, and it might not make financial sense to go deploy a model for low usage. In those instances, it totally makes sense to be able to use a closed source proprietary model because you’re only charged for input data and output data. So it can be really affordable in lower volume projects. But of course, as you do scale it out, that’s where it gets very expensive and the scale tips pretty quickly. It’s where it does make sense to deploy an open source model on your own hardware.
Related: IT Team Modifies Open Source NetBox to Help Manage Hybrid Multicloud]
So it’s nearly impossible to try to automate the process of knowing which model to use and the best way to train or adjust the model for a use case. And so our engineers sit down with customers and look at what exactly are we trying to build, what type of data are we going to be sending to this model? What constraints are there, whether it’s cost or latency. And so we sit down with them to understand what is the best open source model for this particular project. And then from there we can sit down and figure out how should we customize this to improve performance, whether it’s speed, latency, or accuracy. These are all trade-offs that really you kind of have to pick one or the other. So we do our best to help them make the best decisions there.
In this video interview, Hugging Face’s Taylor Linton explains how enterprises can use existing open source software instead of building AI applications from scratch.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Taylor Linton: Predating me joining Hugging Face. We started in 2016 to be a chatbot company. That’s where the fun hugging face name and emoji comes from, because it’s supposed to be a chatbot that’s your friend. So we wanted a friendly brand associated with it. But since I joined Hugging Face in 2021, we started out with 15,000 pre-trained models on the hub. And it was so interesting talking to customers then because it was, Hey, there’s too many models out there, I don’t even know which one to start with. And just seeing that grow from 15,000 to 650,000 in a few years, it really shows how much ai, especially open source has grown.
[Related: Enterprise IT Teams Jump Into AIOps]
It gets even more challenging. So since yesterday when you and I both looked at the models, there’s already 2000 more models. It’s crazy. And the tough part is you can’t really automate these decisions on which model to use. It really is context dependent. You need to know what your cost requirements are, what type of input data that you’re going to be sending to the model, because models are trained on different types of data and you need to try to back into the model that was exposed to your type of domain. During the training. It comes down to latency, the particular task. So these are all things that our engineers can sit down with customers and look at what are we trying to build here? What type of constraints are we working with? And we really walk through them, walk through with them the different trade-offs that you make when you’re picking a different model.
[Related: Role of CIO Expands with Enterprise AI]
A lot of CEOs weren’t even aware of what AI was a couple years ago, and then chat GPT came out and CEO’s kids were doing their homework with it and all of a sudden they started hearing about it. So there’s been a lot of interest in companies to try to take advantage of this technology. When folks talk to hugging Face, it’s because we’re the entire open source ecosystem around ai. And so when they want to explore and take advantage of these open source models, they might go to our hub to go grab one of the 650,000 open source models. But also, once they do grab that model and bring it into their environment, we have quite a bit of maybe 20 different libraries or tools that they use to actually go all the way from building to deploying these models. So that’s really when we get involved with folks is to help them take advantage of open source AI.
[Related: Building a GenAI App to Improve Customer Support]
The community around hugging face and just open source AI is incredible. So the fact that someone might release a paper that introduces a new optimization technique, the fact that the entire community can benefit from that and be able to use it on their models, it’s really exciting and it gets folks pretty motivated to be able to introduce a new novel technique and open source it to benefit a larger group of people. I think that open source AI is critical to be able to keep responsible AI across the world. The biggest reason is because the last thing that people want is to only have a few companies that have access to this technology. The fact that people can build in public and you can know certain data sets that were used to train the models. It’s a great opportunity for companies to make sure they are using models that were trained on permissive data. And bias is also a really big area that folks are looking into because of course, human bias exists in these training sets, and that ends up transferring to the model’s bias. So the fact that people can work together and in a community to really look into these data sets and do their best to try to identify and mitigate bias in these models, that’s very important.
[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]
We partner with quite a few companies, but Nutanix has really stood out to me. It really seems progressive to make sure to offer the best tools and technologies are made available to their customers. So what I’ve learned with GPT in a Box, the fact that they’re integrating it with all of hugging faces, open source libraries, they’re making it frictionless for customers to be able to deploy open source models in their environment. You don’t see that often from, and they’re really going a long way to make sure that the best tools are available for their customers.
In this video interview, NAND Research Chief Analyst Steve McDowell explains how IT decision makers assess strategies and infrastructure needed to run artificial intelligence capabilities.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Steve McDowell: I think we’re all getting AI fatigue. Every product briefing I go to, if it doesn’t have the word AI in it, I’m in the wrong room. That’s all anybody wants to, whether it’s relevant or not. And I think a lot of these technology transitions, whether it’s the internet, whether it’s the smartphone, whether it’s the PC, going way back, there’s a brief burst of time where we’re very excited about the technology pieces, but the value of technology comes in how I use it. So have we hit a wall on AI? No, but I think it’s time to change the conversation. Let’s stop talking about GPUs. We’ll stop talking about who the providers are, and let’s start talking about what are we going to do with it and how’s it going to change my business? Because that’s the interesting conversation. The way I think about AI and we talk about AI, now I’m going to talk about generative AI, but I’ll just call it AI. What’s driving the current moment? I mean, we’ve had AI for a decade, kind of the modern form of AI for a decade. We use it. We use it for predictive analytics, image recognition, retail, whatever. But what’s really impacting what’s about to impact enterprise is generative AI.
We’re seeing a couple of things, and I think 2023 was really the year we figured out how to make these models. And right now we’re going through a kind of rapid phase of how do we make it safe for enterprises? And I look forward over the next 18, 24, even 36 months, and it’s really how do we deploy that in the enterprise from an IT perspective? That means a couple of things. One, I need to pick a partner who’s going to be my generative AI provider. I have a core set of functionality. There’s only a handful of companies in the world that could train these models. It’s OpenAI, it’s meta with Llama, it’s Anthropic, just a handful of these companies. And the way it deploys in the enterprise is I take this large language model and I fine tune it with my own data.
So if I’m an IT guy right now, I’m going down the path of how do I, I’m just trying to deploy. I’m trying to enable my enterprise to use it. And then when it comes to using it, there’s really two pieces. There’s the piece that’s very business focused. How am I going to use AI to enable the next or the next iteration of digital transformation? It’s going to change all our lives, but it’s also, how do I use it to make my own IT operations more efficient? I’ve been to 15 conferences this year, and the theme for 2024 is AIOps, right? Even Nutanix is announcing capabilities around AIOps.
[Related: Pivot Past the Enterprise AI and Cloud Native Hype]
Do I trust it? How do I trust it, how do I deploy it? Where do I use it? A lot of decisions are happening for the poor enterprise IT architect. So what’s happening with ai, we’re trying to figure it out. We’re at the phase where we’ve invented the technology right now, we’re in the enablement phase, then we’re going to start to use it, and then it’s going to drive the transformation. And I look at every kind of big technology, every transformational technology kind of follows this path. Although the timelines are getting much shorter, right? It’s understand it, it’s enable it, it’s play with it, and then it’s going to drive change.
It doesn’t have to be expensive to get into AI as a user and consumer of the technology. And I think where it’s disrupting the tech industry and driving and forcing a lot of this conversation about the technology is it’s very expensive. It’s very complex. It doesn’t look like anything I’ve touched before as an IT guy, or it looks like scientific computing. The cloud guys are solving this for me. They’re managing the infrastructure. They’re buying these expensive GPUs. They’re amortizing the cost over multiple users. Things that I don’t have a budget or capability to do as an enterprise, where that’s causing disruption in the industry is, well, if AI is driving the industry right now and cloud is taking all those dollars, and my company’s name is Dell or HPE or Lenovo, where am I getting my revenue? I mean, the server market was already down. And if those dollars now are being prioritized to cloud, that creates a real dilemma.
[Related: Cloud Vendor Shakeup Puts Focus on IT Resilience]
If I’m looking at this as an IT practitioner saying, where’s the value of AI to my enterprise? I don’t care about hardware. That’s why I like GPT-in-a-Box that the Nutanix is delivering because that’s a software set of capabilities. I can deploy that if that makes sense, right? I can deploy that at the edge, if that makes sense, or I can roll that out in the cloud, if that makes sense. So I think a lot of tech companies are trying to prove their relevance around AI. And I’m not saying they’re irrelevant. It’s just causing a lot of disruption. It’s going to change the way that we think about infrastructure.
There are two pieces of AI. There’s training and there’s inference, right? As a business user, the value is on the inference. An inference is when I take an AI model and I throw some of my own data against it, and it gives me back results, the speed, the time to value, the time to decision is the AI. I say, the closer to your data that the AI is, the faster I’m going to get time to value. And where we look at where, and this is not even a generative AI thing so much when we talk about AI, the most prevalent use of AI is image processing, whether it’s for manufacturing, whether it’s for retail, whether it’s for automotive. If you have a car made in the last 10 years, you have so many sensors in your car, it’s not efficient to take the cameras in your car or the cameras at the seven 11, send those up to the cloud to be processed and send them back down.
[Related: Living Workflows of AI at the Edge]
If I’m running a retail establishment, I might not have internet. It may go down, there may be a storm. I can’t shut my business down when I lose the internet. So by moving those inference functions to the edge, I get all the value of that AI where it makes the most sense. And there’s a couple of things that came together to make this the moment in time where that happens. One is we’ve been talking about the value 5G is going to bring to the world. I get all of this high bandwidth wireless everywhere. We rolled that out without really a killer application. And then we started all this generative AI stuff, and we got really good at AI. And now part of what that did was all the focus on high-end ai, kind of the natural curve of technology is all of the AI inference capabilities required for mid-range, low level ai, dirt sheet, dirt sheet, right? The camera you recorded me on right now can probably track my face. It’s doing inference and facial recognition on a $30 processor. It’s AI at the edge, it’s practical, and it brings true business value. Now, where it gets complicated when we talk about edge, edge is anything outside of the data center where there’s no IT guy, let’s call it that.
[Related: Building a GenAI App to Improve Customer Support]
It’s a couple of edge segments. There’s robo, remote office branch office. To me, those are extensions of your data center. They’re well controlled environments where it becomes really interesting or kind of mass deployments, whether it’s a convenience store, whether it’s factories, whether it’s smart cities, and I have sensors on the lights for traffic control. Managing all of those becomes a challenge. So I’m deploying AI at the edge for these kinds of use cases, but I also have to manage security patches, updates. I got to push new models down. Sometimes I got to bring data back. So what’s the framework look like for that? Well, guess what it, it’s not that different from how I’m doing hybrid multicloud. The boxes are just a lot smaller. So we did this paper and it’s like we’re driving AI to the edge for all the reasons I just said. But then it was really more about, okay, we’re pushing AI to the edge. How do we manage it and how do we make it efficient to manage? And that requires, again, it looks a lot like multicloud, but it may be a more unconstrained environment.
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In this video, Harmail Chatha shares how he helped build a state-of-the-art, efficient data center using software-defined capabilities from Nutanix.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ken Kaplan: When you started building these early on, sustainability was definitely a part of it. You want to make them efficient, but it’s shifted.
Harmail Chatha: So when I first started, sustainability wasn’t even a thing in the industry, right? You heard about sustainability farms and food production and stuff like that, but you didn’t hear about it too much in the tech sector. And of course, larger companies like HPE, and Cisco, have been at it for a long time, but customers really, really never understood it and it wasn’t very top of mind. The way that I got involved in sustainability really started in 2018, not knowing that I was trying to build a sustainable data center, but I’ve actually built one of the most sustainable data centers. And what the premise of that build-out, the architecture then was, we wanted it optimized. We wanted to ensure the amount of power coming into a rack was exactly the amount of power we’re using within the rack, and that the rack wasn’t under provision.
So we’re only using half of the rack, but we’re maximizing full utilization of the rack itself. As I started getting more involved with ESG and specifically the E portion of it, IT consumes a major part of total power in any organization and IT manages the data centers. And we started to realize, wow, we have some really efficient data centers that we architected back in 2018, and we got some not-so-efficient data centers around the world that are more colo retail, a couple of racks here and there.
[Related: Building Scalable, Sustainable Data Centers]
So for us, our multi-megawatt data centers are the most sustainable data centers. We sought out partners that were focused on minimizing their environmental impact by having a lower PUE, and by having innovative strategies around water reclamation. And that really offered us solutions and options around renewable energy as well. So that baseline with that premise set, and now we’re in 2023 and sustainability is top of mind for everybody. For example, we conducted an annual ECI, Enterprise Cloud Index, survey with 1500 global IT leaders, and 92% of the respondents said that sustainability is more top of mind this year than it was last year. With that sustainability starts at the data center. I mean, there are obviously different scopes of sustainability, but IT being the largest consumer makes the biggest impact and can have the most impact on reductions as well.
Ken Kaplan: And this experience that you’re gaining, you’ve done the work that was very successful.
Harmail Chatha: So it was an epiphany to be involved in ESG, specifically the E, and to understand that data centers I built in 2018 are a core part of Nutanix’s environmental impact and the fact that they’re so sustainable. We don’t have to go back and reinvent the wheel because our data centers are already hyper-dense. We’re already maximizing the utilization of power and real estate within the data center. On top of that, we’re already gaining more impact by having renewable energy options and being able to offset or be carbon neutral in our data centers as well. So we got lucky. We’re ahead of the game. It was very smart and intuitive for us to kind of have that focus of optimization, which has ultimately led to more efficiency in the environmental impact.
[Related: Experts Discuss Top IT Sustainability Challenges]
Ken Kaplan: Tell me about your journey, Nutanix’s journey towards sustainable IT.
Harmail Chatha: So Nutanix’s journey really started when the company started itself, right? A step in the right direction, whether they knew it then or not, with hybrid converged infrastructure was this very sustainable approach. So from the get-go we’ve always been a sustainable company per se. But really in 2018 when we revamped our data center strategy, when we went to hyperdense rack design, we even got more sustainable. We only got the amount of power that we needed for our consumption. We only got the right amount of real estate that we needed for our hyperdense racks as well. So those two steps were key in our sustainability journey. But ultimately what we focus on is reducing our infrastructure footprint. We try to consolidate as much as we can. Our standard architecture in the data center is a four-node block, so that way we’re maximizing the power in the space within the rack in itself.
So that was really the third step we took is the consolidation. And then within our platform, we ensured that we weren’t over-provisioning and underutilizing, meaning that we only provisioned infrastructure that we needed for a specific workload or a data set without an overkill of infrastructure in itself as well. We avoided VM sprawl problems as well, ensuring that we’re only running the VMs that we need to run and then hibernating or deleting the rest of the VMs as well. And then ultimately what that led to is a carbon reduction on the back end of it, right? Because our environment is so highly efficient from the data center provider to our power consumption and real estate utilization to our consolidation of infrastructure, eliminating waste of over provisioning, and underutilizing has really led to a much more sustainable carbon-neutral footprint for us in the data center environment.
In this video, learn how a cloud operations expert thinks about power efficiency, software-defined infrastructure and how to get more with less from data center innovation.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ken Kaplan: What does the data center signify to you? What’s the role?
Harmail Chatha: So whether people know it or not, data centers are a very crucial part of everyone’s daily lives, right? All the applications that we have on our mobile phones and our desktops, all the websites that we visit come from a data center. A public cloud is actually a large scale data center in itself as well. So anything that’s tech related goes back to a data house where it’s hosted and hence the services are provided.
Ken Kaplan: Everything we do from health to going to the grocery store increasingly touches the data center.
Harmail Chatha: Absolutely. Health for sure. All the images, all the applications that are used within the hospitals as well. The POS systems and restaurants and grocery stores for sure. Airline industry, when you’re booking your tickets online, that’s all being served by a data center. So really just about anything digital has something to do with the data center.
Ken Kaplan: Part of building the data centers that you’ve helped build: a critical part is the hardware that needs to go in and the power consumption that needs to run it. How is that changing?
Harmail Chatha: So I’ve been in the data center industry since 2005. Back in the day it was small, 42 U racks, maybe three to four kilowatts per rack of power utilization. Maybe you half fill those racks and you had multiple power coming into the rack from a redundancy perspective. But to the point we’re at now, we’re pushing multi kilowatts into a rack. Specifically at Nutanix, we’re between 17.3 to 34 kilowatts per rack. Racks have gone very high density in the sense that we’re able to deploy a lot more compute, especially with hyperconverged infrastructure where you’re able to consolidate storage, networking, and compute into a single server. You’re able to really impact and pack these racks up with compute, which ultimately requires a lot of power and therefore requires a lot of cooling from the data center provider as well. So we call it a hyperdense rack design.
Ken Kaplan: You’re talking a lot about hardware, but software’s playing a key role here, just capabilities you’re being able to take on because of the advancements in software.
Harmail Chatha: Yeah, so software obviously is a huge part of it. One, starting at the lower tier infrastructure consolidation is a first step in the right direction, but ultimately it comes down to the software. Our data centers are software defined from a networking perspective. It’s all white box switches and we control the entire network through software itself. Our infrastructure is all controlled through clusters that are provisioned through AOS, AHV and Prism Central. And what that’s leading us to is infrastructure has a three to five year lifecycle, but we’re actually pushing that lifecycle longer now through our software. Because the software is getting more efficient, it’s better able to utilize the underlying hardware, therefore we’re able to use that hardware on a five to seven, eight year cycle in itself. So software really is becoming the core. Infrastructure is agnostic at the lower tier and software is dictating the lifecycle.
Ken Kaplan: How did you connect with your data center most recently to check on it and see how it’s doing?
Harmail Chatha: Yeah, so our data centers are lights out data centers for the most part. Our bigger data centers, we do have some folks in them, but because they’re so software defined, we don’t have to go to the data center every day. The ones that are lights out through hardware and making sure we have enough redundancy. Then the software being able to manage the VMs. If we do have a hardware failure, there’s really no need for us to go to the data center. And because we got a blueprint for scaling out our data center when we are at a capacity that’s really simple for us to expand. Example would be during the pandemic, we expanded one of our data centers by two megawatts during the pandemic. And that’s no easy feat, right? That’s a lot of physical build out. But because I’ve got such a great team, we got such amazing architecture that was ahead of its time, we’re able to leverage that in addition to the partnerships that we built as well. So there really isn’t a need for me to go to the data center anymore. I think ever since the pandemic started, I’ve probably been once and that was more to check in on the team, make sure they’re doing good in our larger data center. But the remote ones I haven’t been to in several years.
Ken Kaplan: Now, how about virtually? Are you checking it two or three times a day? Are you looking at through your laptop on your phone? What is that part like?
Harmail Chatha: So virtually, we’ve got a lot of alerts set up for the data center as well. So if something does go wrong, a power PDU goes out, cooling is inefficient, we’ve got systems in place where we get alerts that’s at the infrastructure tier from a data center perspective. But for our infrastructure of Nutanix and the cluster that we manage, they’re all software defined as well. We get alerts through Prism Central. We’re able to act on those alerts remotely. Even if there’s a failure of a hard drive or a power supply, we’re able to call some people in and just get that fixed. But the data center interaction is really mostly at the software level now and not at the physical level.
In this video interview, Mat Brown, senior technical marketing engineer at Nutanix, discusses the increasing importance of data center sustainability strategies and the challenges of managing carbon emissions from IT operations.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcription:
Mat Brown: Increasingly, our customers are asking us about sustainability issues and about ESG. They want to know about the carbon emissions related to their IT solutions, whether they’re running them in their own data center at a co-location or in public cloud. These are becoming top of mind for more and more customers all the time.
It’s especially grown up over the last couple of years. What’s really kicked that on has been the energy crisis that’s built up in Europe especially. But then more and more regulation is coming out of the EU and in other places around the world, and that’s driving these sustainability challenges for businesses everywhere.
[Related: Three Trends Defining IT Sustainability in 2024]
Eventually it’s going to be have to embedded into every part of the business they do. I’d expect people will be doing sustainability training, like they do security training, and I expect that people will be reporting on their organization’s and missions like they currently report on their financial practice.
Yeah, so we’ve kind of mapped out four steps on the road to progress, as it were for organizations. Of course, the fundamental basis for everything is a better understanding of impact, but also an understanding of what your business goals are. Who are your allies? Who can you talk to and what resources are at your disposal? From there, you can understand what you need to measure, start implementing the processes to make those measurements, and then on that basis you can then plan and transform and build a business case for future activity as well.
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In this video, Ryan McBrearty, the senior IT infrastructure engineer at global specialty insurer Markle, tells how his team uses Nutanix software and customer services in their quest to cut costs and simplify IT operations.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ryan McBrearty: We are an all-Nutanix shop on-prem. We do have some cloud as well. We have over close to 30 different Nutanix clusters with over 400-plus individual nodes within those clusters. And we have a mixture of ESXi as well as AHV. Some of our AHV clusters are running at 32 nodes, so some big boys out there.
We wanted to cut down operational costs and use automation as a key focus in that. An objective was given to everyone to figure out what processes do we use on a regular basis and how can we speed up those processes and really automate them, especially in the server build space and taking a process that takes two months that has as quick as a window as possible, whether that’s two hours or four hours, beginning with we just had, we want to automate server builds so that we have consistency across that platform. So it was really taking that process and putting a workflow around it.
[Related: Forestry and Land Scotland Trailblazes Private-Public Shift to Cloud]
Automation is a key foundation, so we want to automate everything. If there’s a process that is repeated more than twice, then let’s see what we can do to automate it. So that’s how we’re going to continue to leverage Nutanix self-service. So that and the integration pieces that it has with the other tools in our environment, whether it’s networking, whether it’s security, whether it’s cloud, being able to touch all of those things. Our backup team as well, we want to work towards that software-defined data center.
So there was a lot of back and forth and things had to touch hands multiple times. Whereas once somebody puts in a request, now all of that information is gathered at the beginning, it’s put into at launch of the blueprint and then we just hit go and it just builds it. That shrinks down that timeframe from, again, two months to two hours.
The beauty of self-service is that it will bend to your whims. So if you are comfortable with writing code in a certain language, whether it’s Python or PowerShell, it will allow you to do that the way that you want. The different pieces were very complicated and very challenging to learn and manipulate, and it took a lot of time to go through and really understand how to use those tools where I think the tools today from Nutanix are a lot easier, a little bit more straightforward.
Support with Nutanix has always been great. NCM [Nutanix Cloud Manager] self-service has really been the easy button for us when it comes to automation. It’s been a great tool that has endless possibilities that we have barely begun to scratch the surface on how to use it and how to leverage it. The deeper we dive into it, the more things that we realize that we can do, and I’m excited for the future of what that looks like because again, the possibilities are endless.
In this video, members from the IT team at Organic Valley explain the cost, time and performance benefits of modernizing their data operations with Nutanix.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Joshua Krzych VP of IT (00:00):
I am Joshua Krzych. I’m Vice President of IT operations at Organic Valley, so we have about 2000 small family farms at ebbs and flows from year to year. When I’m making a big decision, such as when we purchase Nutanix, one of the first decisions I have in my head is, what would a farmer think if they saw that this is what I’m spending their dollars on? Because in the end, we’re trying to make them the most profitable small family farmers out there in the world. That’s what we want to do to make sure they’re sustainable and can continue supporting their families and their livestock.
Nick Korte, Dir of Tech Ops (00:30):
My name is Nick Korte and I’m the Director of Technology Operations. When we went to Nutanix, it was a decision around, we went from a traditional three-tier infrastructure, and it worked really well for us for a long time, but we realized that this was a lot less management, a lot less backend nights of doing firmware updates, so simplifying it for our folks and giving them more opportunities to kind of stretch into what could be with Nutanix, whether it’s files or some sort of database as a service operation. It just opens up the door for us.
Joshua Krzych VP of IT (01:13):
Our current ERP solution is there, our warehouse management systems, our supply chain analysis.
Nick Korte, Dir of Tech Ops (01:22):
I look at it from the people perspective of we were spending our network teams and our storage teams and our compute teams we’re all working weekends and just doing updates. There was a fear of breaking something. You have a much more complex infrastructure. When we went to Nutanix, it was like, Hey, we have an opportunity to simplify and we have an opportunity to take that time back for ourselves. If I remember right,
Joshua Krzych VP of IT (01:48):
There was some DBA database maintenance activities that happen nightly that usually took several hours to complete before and after we moved over to Nutanix. It took minutes, maybe not five minutes, but it was way under an hour. Very impressive performance boost there. Moving to the Nutanix platform has really allowed our techs to focus more in engineers, to focus more broadly without having to deeply specialize in an area and then create themselves a backup as well with someone else on their team so they can take a vacation.
Nick Korte, Dir of Tech Ops (02:24):
Really, support was the main thing that we were looking at was, and that’s what really showed Nutanix rose to the top because of the support they work with the hardware, they’re really close with their vendors, so it just was a much better experience, and to me that’s an easy investment for our employees. Again, getting back to how they live their lives. I don’t want them sitting on the phones every night or coming in on a weekend because they can’t figure it out. I want ’em to be at home with their families and enjoying life. I think the main goal will be to get onto the platform a hundred percent, get rid of some of that technical debt that we’ve had and utilized in the past to try and save a couple bucks, and now that we’ve seen the value and being on Nutanix, I think that that’s a great opportunity for us to pivot towards that.
In this video interview, Steve McDowell, principal analyst at NAND Research, talks about how the 2023 economy is impacting IT decision makers.
Find more enterprise cloud news, features stories and profiles at The Forecast.
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Transcript:
Ken Kaplan: It is a very strange economy. You know, there’s all these different indicators and mis-indicators, but in general, because you’ve been following this for a while, there’s a bit of, I don’t know if you want to call it a pendulum or these things come around where IT folks have to make it through these times that are tough, and then there are times when it’s go and grow. Can you just describe where we are today? What’s your take on the economy in these cycles?
Steve McDowell: Yeah, so the macro economy is impacting IT decisions, right? I don’t think there’s a problem with the industry delivering solutions that IT wants to consume.
The challenge is there’s so much uncertainty, whether it’s, you know, what’s happening in Europe or, or, or the continuing saga with China. the, and, and, you know, the interest rates. CIOs are deferring decisions.
Well, CFOs are asking CIOs to defer decisions, and that trickles down. And what that really means practically is the IT guys have to do more with less oftentimes.
And it comes back to, you know, we talk about sustainability and getting the most bang for my buck outta my resources. You know, if I’m not allowed to buy new resources, I really have to do that, sustainability aside. So anything that helps an IT practitioner, you know, better utilize his servers, his storage, his networking, his cloud usage. Man, that’s goodness.
And that’s all because of the economy because, you know, we’re deferring purchasing decisions. And you look at the earnings of HPE of Dell, of all the tier one enterprise OEMs, they’re all talking about this. They’re all saying at some point, second half, maybe, we’ll, we’ll start to see some recovery.
But there’s pent-up demand, but sales cycles are much elongated. And, and, you know, we’re not delivering servers as fast as IT guys want them or that we can do that, and it’s because of the economy.
In this video interview, Steve McDowell, principal analyst at NAND Research explains the challenges of onboarding artificial intelligence capabilities that require robust IT operations.
Find more enterprise cloud news, features stories and profiles at The Forecast.
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Transcript:
Ken Kaplan: Any other trends that you see as we’re going forward that are going to be momentous, especially the, especially during the economy that we have?
Steve McDowell: The biggest, the thing I’m asking most about right now is the impact of AI on IT operations. And it ranges from, you know, how many GPUs can I put in a rack and how do I heat and cool those? How do I power those? Because GPUs are very power hungry and heat-generating more so than CPUs by far. But on the other end, it’s how do I use AI and specifically generative AI to, you know, is it a threat or is it a tool that helps me do my job better? And you know, like any tool it comes down to how you use it. but you know, again, we’re still very early stages of how we’re going to use this in IT operations. But we’re using it for cybersecurity, look for anomaly patterns you know, pattern detection to tell me when there might be trouble. we’re using it for compliance to read, you know, specifications across a number of industries and, and consolidate that into, you know, a set of actions that I need to take. you know, we’re using it to write code, you know, simple pieces of code. Generative AI’s pretty good at writing it if you ask it correctly, right? And that’s kind of the double-edged sword, is asking it correctly and, and ensuring that it’s trained on, on the right knowledge to really be a benefit.
Ken Kaplan: There’s a lot of hype around AI and ChatGPT.
Steve McDowell: There’s a lot of hype around AI, and that impacts it in a couple of ways. One is I need to figure out how to build the infrastructure to support that, because traditional compute does not account for it. So we’re seeing hundreds of experiments, thousands of experiments across organizations, and it has to step up and support those often in short order. so it’s a challenge operationally. The bigger question and where I think a lot of the hype lives is what’s really the power of AI to help me do my job better. You know, generative AI and chat GPT and things like that have a lot of promise, you know, as they exist today. I think they’re interesting tools, but I don’t think anybody’s solving real production problems in it, with those tools, right? What chat G P T does very well is, you know, you train it on a set of patterns, whether that’s code or language or whatever and then it processes those in a way that it can, it can talk back to you, right? Essentially. So I can say, you know, train it on Python code and say, gimme an algorithm that sorts the problem with as they exist today is the training field is very broad. So we don’t know the quality of the data, right? Those of us old in the industry, you know, there used to be a, a garbage in, garbage out saying, and that’s really true of generative ai. So technology has a lot of promise, and I think it’s going to end up being extremely impactful to it. I think it’s just very, very early and, and, you know, I would advise any IT practitioner to well, the IT practitioner already has his expectations set. You have to manage up the chain and set expectations that this is not going to solve all the world’s problems. I can’t go fire all my tech support guys because I have, you know, ChatGPT answering the call. It’s not that simple. So, early days, but let’s keep watching it.
In this video interview, Harmail Chatha, senior director of cloud computing operations at Nutanix talks about sustainability factors and automation capabilities of AI shaping IT strategies.
Find more enterprise cloud news, features stories and profiles at The Forecast.
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Transcript:
Ken Kaplan: What’s your view on AI from a data center mindset?
Harmail Chatha: So AI is going to require a lot of compute, right? It’s a heavy algorithm base, which requires a lot of GPUs, high processing computers. So that’s going to require a lot more power. It’s going to require a lot more real estate, and AI is useful in so many verticals, but ultimately it’s going to be useful in the data center as well. Through AI we’ll be able to self-heal a lot of the problems at the software tier as well. We’re planning to deploy robots in our data centers to do troubleshooting as well, and that’s going to be based on a lot of AI in itself to try to tell the robot what to do, how to fix it, and ultimately we want to be able to plug into the robot Prism Central. Anytime a system does have an issue, the robot already knows and goes and tries to address it itself. So AI, obviously, is going to be tremendous, a vertical that’s going to grow and continue to grow, and it’s going to have a huge impact on data centers and sustainability in itself.
Ken Kaplan: How does it make you feel when you see this wave of AI coming?
Harmail Chatha: So the wave of AI from an infrastructure perspective is going to be tremendous. Right now we’re at a chasm where we’re doing some AI/ML for small level data sets, but now what we’re seeing in the industry, for example, with ChatGPT, that infrastructure is heavy for them to even start the company. They have to deploy so much gear so the systems could learn what to do, how to do, they literally took the entire internet and dumped it into Chat GPT. So the entire internet is run across thousands of data centers. So much power consumption. Data centers consume about 1.5% of global power. And if ChatGPT took the internet into its set and we’re building on AI, that number’s probably going to double in the future as well.
In this video interview, learn how Forestry and Land Scotland discovered Nutanix Cloud Clusters (NC2) as their ticket to moving their IT operations to public cloud.
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ken Kaplan: The forest needs technology?
Nick Mahlitz: Yes. The forests do yes, to manage your forests well and good, to use technology in a challenging environment like Scotland, where it’s very remote and the weather can be quite extreme sometimes. Technology and exploring all realms of technology will only help us better manage Scotland’s forests and our land.
Nick Mahlitz: I manage a team of engineers and technicians and manage our data center where all the magic happens of technology or files or applications and data, et cetera. So we manage that on a day-to-day basis. And then I’m responsible for helping lead and establish our strategic decisions in technology and our future.
Ken Kaplan: And where might technology be used in Scotland?
Nick Mahlitz: Sure. I mean, think of managing and looking after forests where some trees have diseases. The ability to use drones to scour around a forest and use AI and pattern matching algorithms and software to then find the trees that are diseased and stop that spreading is a wonderful use of technology in the realms of a tree in the middle of nowhere in Scotland.
Ken Kaplan: How did you get to this spot in your life, working at this place? How did you get here?
Nick Mahlitz: I was born in the highlands of Scotland and lived there all my life, albeit, I spent a year in Taiwan for quite a while. But I’ve always been interested in technology and for the last 20 years managing and building data centers has been what I do and trying to do it well. And that’s what I now do for Forestry and Land as well.
Ken Kaplan: That world has changed dramatically in the past, especially in 10 years. How do you feel in your career now about that world of IT? How do you feel about it?
Nick Mahlitz: You could feel many feelings about it. It’s challenging, it’s exciting. It’s unknowns. There’s so much happening that we don’t know yet. If we think about the technology over the last 20 years and how all the power was centralized and then the power of technology spread out to the edge, then it came back again into the center, and now we’re seeing this pattern repeat with edge technology and cloud computing, et cetera. So it’s an exciting world, exciting challenge ahead of us. And with the advent of cloud computing, we’ve got some real opportunities here.
Ken Kaplan: Let’s go into the data center and talk about the big challenges that you’re overcoming.
Nick Mahlitz: Yes. So Forestry and Land Scotland are only four years old, so we’ve always been forward-thinking. We have a lot of evangelists of technology in our organization. And so whilst we have our own data center the goal is always to migrate to a full public cloud and enjoy all the benefits that brings us. So one of the projects that we’re working on now is to migrate our entire data center to the public cloud. And that’s where Nutanix are really helping us. They helped us realize with NC2, their product, that migration path can be less challenging than what many other private and public sector organizations are experiencing. To re-engineer all their workloads into cloud takes significant money takes significant resources in terms of skills, which is a big problem in the world, finding and keeping good cloud-skilled people. So yeah, NC2 gives us the ability to migrate our data center to the public cloud using tools that we’re very familiar with. Because Nutanix offers that single management plane to manage both your private and your public instances. So that’s a very exciting project for us, and particularly because we in Scotland have high aspirations for sustainability for our net zero targets for CO emission, CO2 two emissions et cetera. So NC2 really does lend its weight into that area, and that’s very important, very important for us. So an exciting time where we can migrate our data center to the cloud and free up our digital technologists and our IT skilled people to perhaps concentrate on other things like AI and automation and other new exciting technologies.
Ken Kaplan: Let’s talk about sustainability and NC2. Tell me how it’s helping you particularly achieve some goals or set some new strategies for sustainability.
Nick Mahlitz: Yes. So we’re actively looking at the metrics and analytics behind what we currently have in our private data center. And then with Nutanix’s assistance, we know what that will look like with NC2 in the cloud. And there could potentially could be up to a 40% reduction in our footprint, which is a big savings. So whilst we don’t have the analytics yet, it’s a report I’m looking to create in June this year. NC2 really does help with that. And as we try to meet Scottish government targets for sustainability and we’ll do that via our vehicle management and our buildings management. But our data center is another big area where we can reduce that by using public cloud and NC2.
Ken Kaplan: Do you, do you ever get a sense that what you’re doing can also help other government agencies?
Nick Mahlitz: Yeah, yeah, very much so. We meet frequently with other agencies and the Scottish government in cloud communities where we share what each of us are doing in that area. And there’s been so much good work done by the Scottish government and realizing all the technologies to migrate to the public cloud, but what we are doing, no one else has done. And so using NC2 to trailblaze in this and show the benefits to my peers in our government organization, I think it’s key for me if I can help reduce costs and manage those costs in a fixed way. And if I can avoid the challenges around recruitment, which is a particularly challenging thing in the UK to reduce our footprint, then I think other organizations in the government and public sector should also look to and analyze both your native cloud approach and using technology like NC2.
Ken Kaplan: I mostly want to just capture what it is that you do. It’s interesting because we don’t think about, you know, the forest and technology and that you’re actually trailblazing. You said that those great words.
Nick Mahlitz: If you think about what’s important to people nowadays, you know physical and mental health and wellbeing are very much on the forefront of the organization. You’ve got to look after your people. If you don’t invest in your people and lose them, then you know, the organization suffers. So, Forestry and Land Scotland wants to invest in their people. We want to look after the people of Scotland, too. So, you know, there are areas that we do in terms of creating visitor centers and tourist places, and those green spaces where the family can go out for a day and enjoy a wonderful day with a picnic and enjoy the beautiful scenery that Scotland has. That’s very much also in our hearts. And again, technology is playing a part in that because we can deliver services to these rural areas that are in isolation in a better way with technology, which is a fantastic thing to do, to look after your people.
Ken Kaplan: Stepping back. The land of Scotland is the treasure, it’s precious. What’s your mission with all this?
Nick Mahlitz: Yeah, our mission is to look after our land and enhance the management of the land and forest for the people of Scotland and manage it on their behalf. Really, we’re just custodians of this. And give them the beautiful scenery that Scotland has and give it to the people to enjoy. But we also have the responsibility to not only manage that but to manage that in a sustainable way. So managing our forests in the best way possible, managing our land and creating renewable energy to help that sustainability piece is very important to us. So that’s our mission, to really provide what Scotland is to its people, but also really help our government and the world for climate control because it’s one of our big, big strategies that we take.
Intel Corporation embraced Digital Twins to transform their microprocessor fabs. Now Intel engineers have packaged this technology and made it available to manufacturers around the world.
Intel Automated Factory Solutions is a suite of products that embody the experience Intel has gained implementing Digital Twins. Designed specifically for high-end technical manufacturers, these products are custom-fit to meet each customer’s unique operational challenges. Expert Intel engineers work on-site with customer stakeholders each step of the way, from initial product planning up until all systems are online, running, and producing.
As Intel’s successful adoption has proved, Digital Twin technology can deliver tremendous benefits and cost savings for manufacturers today, and tomorrow.
In this video, see an early version of a modeling tool that estimates carbon emissions and power consumption of data center operations. This data can help shape strategies and inform decisions around IT sustainability.
Related: Building Scalable, Sustainable Data Centers
Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Mat Brown: Cheers, Ken. I’m Mat Brown. I’m the technical lead for data center and Sustainability at Nutanix. We’re here today in Chicago for Nutanix dot X 2023, and I’m gonna show you a little bit about the Nutanix Carbon and Power Estimator tool, which is an educational tool we’ve been developing and currently have it in early access. We’re previewing it here and it’s to help our customers better understand their environmental impact of their IT systems.
Ken Kaplan: Okay, so what do, what do we see on the screen?
Mat Brown: So on the screen, we’ve simplified down these four inputs. On the left-hand side here we have a type solution type. We have the sort of workload for that solution type in terms of the number of virtual machines. We have the p e that’s the power usage effectiveness or how efficient the data center is in terms of delivering power to workloads. And then we have the location here at the bottom, so the location of where that workload’s running. And that’s really important because of the carbon intensity related to the electricity grid that the data center would pull its power from.
Ken Kaplan: Just describe to me again, why would people want to use this or why do they need it?
Mat Brown: This is mainly an educational tool so that people can better understand the different aspects of their workload, where it is, how efficient their data center is and where it’s located and what that overall adds up to.
Ken Kaplan: Okay. Can you show me what kinds of systems you have in here, and what you can analyze? Yeah,
Mat Brown: Sure. So we’ve got a few different solutions. These are all based on Nutanix-validated designs. No, we’ve generalized and simplified things here, a great deal. But because of the way that Nutanix scales really linearly and because of the great work our teams have been doing around the validated designs, we’ve been able to base them on these core validated designs here. So we’ve got generally virtualized applications, databases using NDB (Nutanix Database Services) and end user computing, virtual desktops, and cloud native Kubernetes as well. So we’ll just pick one of these and then in those validated designs there is a config for a bm, a typical CPU and RAM. And from that, we can then decide how many we want to apply to a workload. So here I’m giving a 4,000 virtual machine workload that’s based on a certain configuration that’s documented in our validated designs. We can then have a look at the data center, p u e.
As Broadcom’s acquisition of VMware nears completion, IT leaders are hedging their investments, according to Steve McDowell, principal analyst at RAND Research.
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Find more enterprise cloud news, features stories and profiles at The Forecast.
Transcript:
Ken Kaplan: Last time we talked to you in an interview, we had talked about Broadcom.
Steve McDowell: I know we’re at a Nutanix event, but I’m going to say I’m a fan of VMware and I think the products they bring to market add value to IT organizations and, and, and help simplify their life. Um, but at the same time, I think there’s a question as this acquisition drags out a number of questions around, you know, how safe my investment in VMware is. We don’t know what Broadcom is going to do with that asset when they have it. Broadcom has a long history of acquiring companies that they feel have reached kind of commodity level, right? What concerns me when I look at VMware and the Broadcom acquisition is, is there going to be a drain on innovation, right? We already see people leaving VMware. You know, what’s going to happen with pricing? you know, this is Broadcom is going to take VMware from, you know, an innovative kind of leading player in the industry and try to fit that into a broader portfolio. And that’s causing a lot of confusion. That’s causing a lot of uncertainty, not just among IT guys, but about, you know, those of us who track the industry and those around the industry. Um, you know, so there’s questions in my mind if I’m deploying VMware, is that a safe bet moving forward? I’m not saying don’t do it, but I’m saying as you do, you know, make sure you hedge a little and make sure that you’re really safety proofing your investment in those assets. Right?
Ken Kaplan: Has anything changed in your thinking about it now?
Steve McDowell: Well, here’s what I’ll say. So Broadcom is saying all of the right things, um, about their acquisition of VMware. The challenge is that this thing continues to drag out and drag out, and the uncertainty within VMware itself is causing a little bit of a brain drain, right? We’re seeing an exodus of VM employees into other organizations. That concerns me a little bit. So even if they continue to invest in r and d, there’s going to be a bump. There’s going to be a bump in their roadmap until this acquisition closes, I don’t expect we’re going to see, you know, significant new innovation out of VMware. And then post-acquisition, there’s always a period, right? So, you know, there’s fear, there’s uncertainty, there’s doubt about the future of VMware under Broadcom. I think that’s well-founded now. I don’t think VMware’s going anywhere, right? But are they going to be the company that we thought they were two years ago? I’m not so sure.
IT Best Practices: In this fourth episode of Intel’s Path to Cloud series, Phil Vokins, Cloud Services Director with Intel Americas is joined by Intel IT Principal Engineers Sachin Ashtikar and Jose Alonso Cambronero Ramirez along with Joe Carvalho Principal Engineer with the Cloud Enterprise Solutions Group. We know that Enterprises and workloads have been moving towards Containers for many years. In this discussion, the group shares how Intel IT has embraced Containers as a Service to add flexibility, drive new capabilities, accelerate development, and increase efficiency. Listen to learn more about this transformative as-a-Service model to see what other benefits it can offer for your organization and customers.
To download the white papers and get notified of the next episode release, please register here:
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See also: IT@Intel: Cloud Containers as a Service – Wins, Trends and Strategies:
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In this video, explore a state-of-the-art Nutanix data center built with highly integrated hardware and hyperconverged infrastructure (HCI) software. Harmail Singh Chatha, senior director of global cloud operations at Nutanix, explains what went into building the data center. His team wanted to build a state-of-the-art data center that could scale sustainably to meet present and […]
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Increasingly, employees and students reply on streamed applications rather than having apps run directly from their computer’s hard drive. In this video animation, learn how end user computing, a catch-all phrase, is changing as technologies like desktop as a service (DaaS) and virtual desktop infrastructure (VDI) stream experiences from the cloud or a data center. […]
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Today’s monolithic mainframe approach to computing will soon be shattered and dispersed because that’s the nature of computing, according to Nutanix CEO Dheeraj Pandey. This animation brings to life a talk Pandey gave to technology analysts at .NEXT in Anaheim, California in 2019. Follow the enterprise cloud tech revolution at The Forecast by Nutanix.
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Ofer Lior demonstrates the Intel Data Center Management tool showing real-time power and thermal monitoring, sub-component failure analysis and out of band real-time utilization data, server count per rack, power usage, as well as many other features.
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In this Intel Chip Chat Under the Hood video podcast: Jonathan Stern, Storage Solutions Architect at Intel, discusses how Non-Volatile Memory Express (NVMe) over Fabrics tremendously increases storage performance and scalability in the data center. He illustrates how NVMe over Fabrics allows data center architects to build the foundation of the next generation data center, […]
In this Intel Chip Chat Under the Hood video podcast: Jonathan Stern, Storage Solutions Architect at Intel, provides an under the hood look at the Storage Performance Development Kit (SPDK). He highlights how the adoption of SPDK components enables customers to realize incredible efficiency and storage performance advantages which will only grow as the next-generation […]
IT Best Practices: You ever join your coworkers in a conference room and have difficulty sharing material on screen? At Intel, we’re deploying an Intel Unite solution to help resolve this problem. Here’s how it works… I’ve entered a room that has Intel Unite and I walk up and see that there are simple instructions […]
The Network Transformation Video Podcast Series, The Network Transformation Video Podcast Series, hosted by Intel’s Jim St. Leger, tackles some of the most pressing challenges and opportunities in transforming the network. This episode provides an introduction to and highlights the benefits of Container technology. Containers reduce the complexity and ease the creation and deployment of […]
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The Network Transformation Video Podcast Series, hosted by Intel’s Jim St. Leger, tackles some of the most pressing challenges and opportunities in transforming the network. This episode covers high-efficiency packet processing, which reduces the strain of managing data flow on your network and facilitates the streamlined transfer of information. Advances in Network Functions Virtualization (NFV) […]
The Network Transformation Video Podcast Series, hosted by Intel’s Jim St. Leger, tackles some of the most pressing challenges and opportunities in transforming the network. This episode covers how open source solutions are changing the networking landscape. Open source has long played a key role in the ongoing evolution of Software Defined Networking (SDN) and […]
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The liquid immersion cooling technology immerses entire IT devices such as servers and storage devices in liquid coolant and circulates the coolant to cool the devices. It uses a liquid with high heat transport performance to improve the efficiency of the entire cooling system, thereby reducing power consumption. The technology also saves space through high-density […]
What is Oracle Developer Studio? Watch this two-minute lightning talk from Ikroop Dhillon, Principal Product Manager to learn more. For more information see: oracle.com/tools/developerstudio
Healthcare company Integra Life Sciences discusses using an Oracle PaaS Cloud hosting solution from NTT DATA and Dimension Data on SuperCluster to securely accelerate enterprise application deployments after an acquisition. See more posts from NTT DATA and other vendors from Oracle OpenWorld 2016 in San Francisco.
The Network Transformation Video Podcast Series, hosted by Intel’s Jim St. Leger, tackles some of the most pressing challenges and opportunities in transforming the network. This episode covers Telco Cloud, which takes advantage of the unique distributed nature of telco data centers and networks, allowing ecosystem players to capitalize on improvements in performance, reliability, security, […]
The Unstructured Data Analysis Series explores unstructured data analytic capabilities using Oracle’s Big Data Discovery. Oracle’s Big Data Discovery is a great tool for gaining visibility into unstructured data. This session focuses on using the group value feature to combine similar values into one attribute. See more posts from Baker Tilly and other vendors from […]
The impact of substantial government cuts combined with increased service pressures meant Reading Borough Council needed to reduce administrative costs to allow them to maintain vital frontline services. Fujitsu listened to the Council’s challenges, assessed the situation and recognised what solution would present best value in terms of efficiency and savings. The 17-year partnership between […]
The Unstructured Data Analysis Series explores unstructured data analytic capabilities using Oracle’s Big Data Discovery. Oracle’s Big Data Discovery is a great tool for gaining visibility into unstructured data. This session focuses on how to use whitelists to extract meaning form discussion forum posts and gain insight from external forum data. See more posts from […]
Data is all around us. At Fujitsu, we know that when we connect it with people, amazing digital transformations can happen to benefit business and society. Find out more: fujitsu.com/transform. See more posts from Fujitsu and other vendors from Oracle OpenWorld 2016 in San Francisco.
The Network Transformation Video Podcast Series, hosted by Intel’s Jim St. Leger, tackles some of the most pressing challenges and opportunities in transforming the network. This episode covers the case for business transformation for Communications Service Providers (Comms SPs), describing how advances in virtualization and cloud technologies are shifting the landscape. Software Defined Networking (SDN) […]
More and more of the differentiation that companies seek is not based on their product or service; it’s based on the experience they’re providing to their customers. But it’s becoming particularly difficult for organizations who have had product differentiation in their history to differentiate through customer experience. Watch and learn about three trends that are […]
Red Hat has built its reputation with Red Hat Enterprise Linux, helping enterprises take advantage of open source innovation by successfully migrating from Unix to Linux. But it doesn’t stop there. Learn more about Red Hat’s comprehensive portfolio and the value of a Red Hat subscription. See more posts from Red Hat and other vendors […]
G&W Laboratories benefits from Oracle’s Learn Cloud solution to train and qualify their workforce to be FDA compliant. See more posts from NTT DATA and other vendors from Oracle OpenWorld 2016 in San Francisco.
Microsoft Azure Information Protection is a new solution that makes it simpler to classify and protect information, even as it travels outside of your organization. You’ll see the new options that let you define how your users can classify their documents and emails during the normal course of their work. Find out how you can […]
The 2016 Internet Security Threat Report (ISTR) provides an overview and analysis of the year in global threat activity. It is compiled using data from the Symantec Global Intelligence Network, which our global cybersecurity experts use to identify, analyze, and provide commentary on emerging trends in the threat landscape. Symantec discovered more than 430 million […]
In this video, we walk you through the technology behind HPE Synergy, the industry’s first composable infrastructure platform. HPE Synergy helps CIOs maintain the structure and security of traditional IT while still having flexibility and speed for new applications. Learn more at: hpematter.com
Highlights of John Fowler, Oracle Executive Vice President of Systems, announcing major new additions to the SPARC platform that for the first time bring the advanced security, efficiency, and simplicity of SPARC to the cloud. For more information see: oracle.com/sparc
The management team at Family HealthCare Associates discusses their use of the Dimension Vista 1500 System and ADVIA Centaur System from Siemens Healthcare Diagnostics. Explore full portfolio of Siemens clinical lab systems: healthcare.siemens.com
Microsoft Mechanics: Sr. Product Manager, Javier Nino from Microsoft, offers a quick overview of the new free Microsoft IT Pro Cloud Essentials program and its benefits including: access to professional training; extended Cloud services trials; free credit for Microsoft Azure; career guidance and prioritized expert level support. If you are an IT Professional, you can […]
VMware’s vice president and general manager, Cloud-Native Applications Business Unit, Kit Colbert, reflects on the first year since VMware introduce its cloud-native apps team.
The IT Automation Cloud 1.0 validated design supports active/active dual-regions with each region being comprised of a single availability zone. In this design, the foundation components, such as the management and edge pods are deployed in each region were their specific instances of vCenter Server, Platform Services Controllers, NSX Managers, Controller Cluster and Edges, and […]
Protect your organization against unauthorized access, compromised email, and document tampering. The leading provider of user-friendly cloud-based PKI, Symantec delivers a proven and globally trusted solution used by the Fortune 500 and government agencies. For more information visit: symantec.com/managed-pki-service
At Tuesday’s opening General Session, hear from Hewlett Packard Enterprise President and Chief Executive Officer, Meg Whitman as well as Hewlett Packard Enterprise senior executives Antonio Neri and John Hinshaw on harnessing hybrid infrastructure to increase agility and deliver great customer experiences. Hear about the exciting innovation from Hewlett Packard Enterprise that bridges your applications […]
3D Animation Software: Using a Kinect for Windows v2 device and Mixamo’s Fuse software you can scan yourself to create a game-ready 3D avatar. Game developers and gamers are using this workflow to create likenesses of themselves to use in 3D projects and to play directly in games. Mixamo saves countless hours of work for […]
Intel IQ: Ahead of the televised 2016 Oscars, the Academy Awards for Scientific and Technical Achievement honored innovators using new technologies to advance the movie making process. This year, Brian McLean and Martin Meunier won for their innovative use of 3D printing, which has advanced character animation in stop-motion filmmaking. Next year, it could be […]
In this Intel Chip Chat Under the Hood video podcast: We are seeing an explosion of data being produced today and in order to get real-time analysis and make this data come to life we need something revolutionary that will change memory and storage as we know it. In this video Intel’s Mike Ferron Jones […]
IT Best Practices: Even with the proliferation of device types, Intel IT continues to adhere to the fundamentals of our PC Refresh Strategy. Listen to John Mahvi explain what we believed in the past and what we believe now.
Intelligent Networking: Software Defined Networking and Network Function Virtualization Changes Everything.
Intelligent Networking: Intel ONP for Servers is designed to make it easier to test and deploy SDN and NFV solutions.
Intel Chip Chat – Network Insights audio podcast with Allyson Klein: Lynn Comp, Director of Market Development Organization, Network Platforms Group, Intel, explains the Intel Network Builders Fast Track initiative and how it will help deliver the promise of network transformation. A fully integrated telecommunications cloud offers service providers a path to new opportunities, new […]
Intel Chip Chat – Network Insights audio podcast with Allyson Klein: Sandra Rivera, a Vice President in the Data Center Group and General Manager of the Network Platforms Group at Intel, explains the challenges and benefits of the transformation taking place in the network and the rich, immersive user experiences it brings. She shares Intel’s […]
In this Intel Chip Chat Under the Hood video podcast: Deploying to a cloud data center can be a lot like moving into a new neighborhood. No one wants “the noisy neighbor”. Intel’s Billy Cox takes an under the hood look at data center performance and trust and how Intel can help. For more information […]
Intelligent Compute: Intel executives discuss how they are leading in 5G with small cells and MEC technologies
In this Intel Chip Chat Under the Hood video podcast: Today’s data centers face new and unexpected threats every day – from corporate espionage artists to terrorists to natural disasters. While data center security has always been a top priority, with the growing use of social media, mobile technologies, analytics and cloud computing, a resilient […]
Software Defined Infrastructure: Intel is enabling NFV to deliver on its flexible and efficient infrastructure promise. This demo showcases multiple Data Plane Development Kit (DPDK) packet processing virtual machines (VMs) running on industry standard high volume servers.
Software Defined Infrastructure: Sandra Rivera, VP, Intel Data Center Group, at Light Reading‘s 2015 Big Telecom Event, talks about accelerating transformation in the network with Intel Open Network Platform (Intel ONP) and the growing Intel Network Builders ecosystem.
Intelligent Networking: Virtual video transcoding for OTT and live/linear video content: Delivering maximum streams/density per RU for mobile video consumption as well as speech transcoding for cloud-based media processing for C-RAN and vRAN network functions, shown on Artesyn GPU-accelerated SharpStreamer edge platforms with Vantrix software.
Intelligent Networking: If you use carrier-grade as your design-point you realise there are a lot of enterprise segments you can tackle as well, such as financial services, Sandra Rivera explains to Martyn Warwick. She talks about how she’s glad that infrastructure is getting its turn in the limelight this year at MWC; how important IoT […]
Intelligent Networking: With 23 NFV proofs of concept and a growing partner ecosystem, HP is demonstrating to communications service providers the value and viability of NFV solutions. Having access to the open source and open software community is also benefiting providers and network operators, as work continues on defining the necessary NFV frameworks and standards.
Intelligent Networking: NFV is a clear trend and will profoundly change the telecom industry, Leo Ma tells Martyn Warwick. The hardware is being de-coupled from software and Huawei is ready to embrace this new principle with Fusion Engine, but telecom performance requirements must be considered. The first requirement in this new world is to be […]
Intelligent Networking: ETSI is playing a fundamental role in the development of NFV standards, ensuring interoperability between device vendors and collaboration with other industry groups. As work progresses during the ninth NFV meeting in Prague, TelecomTV asks the ETSI Director General about the accomplishments to date and his expectations for the adoption of the the […]
Intelligent Networking: Ericsson has partnered with Intel to add software and hardware to enable highly governed, secure and hyperscale cloud, as part of a transition to software defined networking.
In this Intel Chip Chat Under the Hood video podcast: Non-Volatile Memory Express (NVMe) technology allows you to unlock the incredible performance, low latency, and low power benefits of PCIe SSDs. In this Under the Hood episode, Senior Principal Engineer Amber Huffman illustrates how the combination of NVMe specification and Intel Solid-State Drive Data Center […]
Software Defined Infrastructure: The journey of cloud architecture evolvement combined with cost saving consideration and how we do the trade-off based on limited resources to finally achieve an Enterprise level high availability OpenStack production with low cost.
Software Defined Infrastructure: You are a cloud user who wants bare metal for performance forging the security benefits of virtualization. All the OpenStack services, such as, Nova, Keystone, and Glance, all run on bare metal.
Software Defined Infrastructure: Telemetry and full-stack instrumentation, combined with analytics and actuation and the orchestration system itself enables service providers and application developers to easily access advancements and specialisations in the platform and middleware, ultimately benefiting the consumer with better service at lower cost.
In this Intel Chip Chat Under the Hood video podcast: How can we create scalable, reliable, and responsive data centers and networks that will enable innovative new digital services? Intel’s Sandra Rivera takes an under the hood look at Dynamic Resource Pooling and how Intel is enabling the transformation to Software Defined Infrastructure. To learn […]
Intelligent Storage: Swift Project Erasure Codes–container model for grouping objects within code.
Intelligent Networking: SDN is designed to reduce costs and streamline processes to make networks more flexible, efficient, and easily managed.
Software Defined Infrastructure: OpenStack is an open source project to enable enterprise and service provider building their own IaaS infrastructure, and it is fundamentally changing the landscape of enterprise IT.
Intel IQ: Meet the team helping with drug discovery and research at the San Diego Supercomputing Center at UCSD When Professor Ross Walker explains what he does for a living, he says he’s on the cutting edge of drug discovery research using supercomputers. But when he explains why he does this work, he says it’s […]
Intelligent Networking: Software Defined Infrastructure (SDI) architecture addresses the compute, network, storage, security, energy, and manageability aspects of the infrastructure.
Intelligent Compute: Learn about Intel’s vision for storage in the data center, how frameworks like SW defined storage will simplify the storage infrastructure, improve efficiency, and enable rapid deployment of new services.
In this Intel Chip Chat Under the Hood video podcast: How will the data center of the future automatically diagnose and solve issues on its own? Intel’s Das Kamhout takes an under the hood look at Intelligent Resource Orchestration and how Intel is working to enable the intelligent data center of the future. To learn […]
Intelligent Networking: Solve the problem of increased network demands and the growing need to support virtualization with 10GbE Intel Ethernet.
Intelligent Networking: The world is moving to 10 Gigabit Ethernet. Explore the four technology trends and three data center changes that are driving the transition and helping 10 GbE go mainstream.
Intelligent Networking: Intel transforms network services with software-defined platforms that enable fast service deployment and more revenue opportunity.
In this Intel Chip Chat Under the Hood video podcast: As more and more businesses go online it is important that their digital services are based upon a foundation that’s scalable, cost effective, and performant. Let Intel’s Rob Hays take you under the hood of Workload Optimized Silicon and Intel’s vision for the digital services […]
Intelligent Networking: Steve Price, GM Communication Infrastructure Division, Intel, gave a keynote speech at 4G World about data growth and the evolution of devices.
Software Defined Infrastructure: Software Defined Infrastructure: Intel ONP for Servers is designed to make it easier to test and deploy SDN and NFV solutions.
Intel Chip Chat – Under the Hood: More data is available to organizations than ever before, so a key opportunity is converting that raw data into actionable insights. Intel’s Ron Kasabian looks under the hood at how Intel is building the foundation for Pervasive Analytics and Insights. To learn more, visit: intel.ly/1JHnYY5
Software Defined Infrastructure: See how the Intel Ethernet Flow Director works with the Intel Ethernet Controller XL710 product family and other Intel products to correctly direct Ethernet packets to the right processor core for faster results.
Software Defined Infrastructure: The Intel Ethernet Controller XL710 supports data center modernization from 10 to 40 Gigabit Ethernet for a flexible, agile, and efficient platform for the rapid deployment of services for the enterprise, cloud, and telecommunications.
Software Defined Infrastructure: The kit lets engineers maximize packet throughput and workload performance while minimizing development time.
Software Defined Infrastructure: Intel architecture enables the next generation network with a standardized platform that increases flexibility, scalability, and performance.
Software Defined Infrastructure: Intel General Manager Bev Crair and EMC VP Boaz Palgi discuss how Intel and EMC ViPR cloud storage solutions help manage and extract value from data through software-defined data center networks and storage.
Software Defined Infrastructure: Intel IT is addressing the quick deployment challenge by establishing PaaS for the developers of custom applications in Intel’s enterprise private cloud. With OpenStack at its core, Cloud Foundry provides a higher level of abstraction through the application runtime which speeds deployment and reduces support burden. Intel IT has deployed Cloud Foundry […]
Software Defined Infrastructure: Derek Sellin, director of data center software marketing for Intel, speaks from the OpenStack Summit about how Intel collaborates with the open source community to drive OpenStack readiness and also works closely with downstream partners to ensure enterprise adoption.
Software Defined Infrastructure: Communication service providers embracing Network Function Virtualization (NFV) are endorsing OpenStack as providing the common API Service Framework required to deliver their applications and services with a lower time to market.
Intelligent Networking: Saar Gillai, HP Senior VP, says his two most often heard questions are: ‘How ready is the cloud?’ and ‘What NFV app should we do first?’ The good news, he says, is that the carrier grade scaled-out cloud is ‘just around the corner’ and there will soon be early deployments beyond the Proofs […]
Intelligent Networking: The Communications industry is at an inflection point, and Intel innovations are enabling transformation. AT&T’s John Donovan, senior executive vice president, AT&T Technology & Network Ops, explains how Intel technology is enabling the transformation of AT&T’s network.
Intelligent Compute: Increasing packet processing performance with Intel Xeon processor 5500 series for faster time-to-market.
Intelligent Networking: Sandra Rivera, VP, Data Center Group & General Manager, Network Platforms Group at Intel, speaks from Big Telecom 2015 in Chicago about how Intel is accelerating transformation in the network. She discusses how Intel’s Open Network Platform (ONP), which received Light Reading’s Leading Lights 2015 award for most innovative NFV product strategy, and […]
IT Best Practices: In this video, we discuss execution excellence, business value creation, and being a catalyst for business organization transformation. Also, thoughts on innovation, risk and how Intel is looking at disruptive technologies such as the Internet of Things in today’s changing business landscape.
Intel’s Network Builders Program was launched last year, designed to accelerate the SDN and NFV industry. The aim is to work closely with the industry ecosystem, to innovate around Intel’s technologies.
Intelligent Networking: HP’s OpenNFV is going well, Werner Schaefer tells Martyn Warwick. The effort is spread across HP’s global labs, including those at Grenoble, Houston, Fort Collins, Tel Aviv and Seoul. The initiative now has nine partners including BT, Brocade, SK Telecom and Wind River and HP has already invested heavily in both equipment and […]
Software Defined Infrastructure: DPDK is a software library that lets you do packet acceleration on general-purpose, x86 Intel processors. DPDK means you can bring big packet workloads onto a general-purpose processor and then use the additional cores on the chip to work on other things.
Software Defined Infrastructure: IDF 2014: Maxta software defined storage solutions. New architectures: Grantly launch, Intel Xeon processor E5V3, boosting performance to support storage platforms.