Advancement in ML Workflows: You've been around the ML world for long enough to have seen how much workflows, tooling, frameworks, etc. have matured and allowed for greater scale and access. We'd love to reflect on your personal journey in this regard and hear about your early experiences putting models into production, as well as how you appreciate/might improve the process now. Data Professional Diversity and MLOps: Your work at fast.ai, Kaggle, and now with NBDEV has played a huge part in supercharging a diverse ecosystem of professionals that contribute to ML-like ML/data scientists, researchers, and ML engineers. As the attention turns to putting models into production, how do you think this range of professionals will evolve and work together? How will things around building models change as we build more? Turning Research into Practice: You've consistently been a leader in applying cutting-edge ideas from academia into practical code others can use. It's one of the things I appreciate most about the fast.ai course and package. How do you go about picking which ideas to invest in? What advice would you give to industry practitioners charged with a similar task at their company?
In this episode
Founder and Researcher, fast.ai
Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai. Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top-ranked participant in international machine learning competitions for 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open-source projects. He has many media appearances, including writing for the Guardian, USA Today, and The Washington Post, appearing on ABC (Good Morning America), MSNBC (Joy Reid), CNN, Fox News, BBC, and was a regular guest on Australia’s highest-rated breakfast news program. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is a co-founder of the global Masks4All movement.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.