Josh Tobin holds a CS PhD from UC Berkeley, which he completed in four years while also working at OpenAI as a research scientist. His focus was on robotic perception and control, and contributed to the famous Rubik's cube robot hand video. He co-organizes the phenomenal Full Stack Deep Learning course and is now working on a new stealth startup. Learn more about Josh: http://josh-tobin.com/ (http://josh-tobin.com/) https://twitter.com/josh_tobin_ (https://twitter.com/josh_tobin_) Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46 (https://mlengineered.ck.page/943aa3fd46) Comments? Questions? Submit them here: https://charlie266.typeform.com/to/DA2j9Md9 (https://charlie266.typeform.com/to/DA2j9Md9) Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI) Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/) Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen) Timestamps: 01:32 Follow Charlie on Twitter (http://twitter.com/charlieyouai (twitter.com/charlieyouai)) 02:43 How Josh got started in CS and ML 11:05 Why Josh worked on ML for robotics 15:03 ML for Robotics research at OpenAI 28:20 Josh's research process 34:56 Why putting ML into production is so difficult 44:46 What Josh thinks the ML Ops landscape will look like 49:49 Common mistakes that production ML teams and companies make 53:11 How ML systems will be built in the future 59:37 The most valuable skills that ML engineers should develop 01:03:50 Rapid Fire Questions Links https://course.fullstackdeeplearning.com/ (Full Stack Deep Learning) https://arxiv.org/abs/1703.06907 (Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World) https://arxiv.org/abs/1710.06425 (Domain Randomization and Generative Models for Robotic Grasping) https://deepmind.com/blog/article/neural-scene-representation-and-rendering (DeepMind Generative Query Network (GQN) paper) https://arxiv.org/abs/1911.04554 (Geometry Aware Neural Rendering) https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-104.pdf (Josh's PhD Thesis) https://www.youtube.com/watch?v=x4O8pojMF0w (OpenAI Rubik's Cube Robot Hand video) https://www.wandb.com/podcast/josh-tobin (Weights and Biases interview with Josh) https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/ (Building Data Intensive Applications) http://creativeselection.io/ (Creative Selection)