Fast.ai, AutoML, and Software Engineering for ML // Jeremy Howard // Coffee Session #47

Coffee Sessions #47 with Jeremy Howard, Fast.ai, AutoML, Software Engineering for ML.

//Abstract
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?

// Bio
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 of 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 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.

// Other Links:
jhoward.fastmail.fm
enlitic.com
jphoward.wordpress.com/

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Timestamps:
[00:00] Introduction to Jeremy Howard
[02:11] Jeremy's background
[03:10] Workflow
[06:12] The scientific process
[12:36] On Laziness
[12:59] Platform development
[19:53] Balancing API
[22:57] Moment of inefficiency
[27:42] Helpful tactics
[29:05] University of tools evolving
[30:08] Make deep learning more accessible.
[35:52] Other people doing good work
[36:37] Focus on outcomes rather than winning.
[41:10] Resources to solve problems
[43:30] Jupyter notebooks
[47:20] Putting Jupyter notebooks into production
[48:42] NBDev
[51:20] Frustrations with putting ML into production
[55:28] You're not gonna get everything right

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