Leading AI/ML Teams with Craig Martell Head of LyftML @ Lyft #27
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Craig Martell shares the biggest mistakes leaders of ML teams make, what to do if you have no experience leading an ML team, key skills your ML team needs, plus different models/approaches to building an ML team. You’ll hear the most expensive and time-consuming parts of ML, how to estimate timelines, unique tech debt, and how to manage expectations.

*“If I had to give one piece of advice about starting AI in your company, one of the first people I would hire is a really great data scientist, even if they can't code. Just so they're the one who's going to start training you and helping you think about, how to gather data, how the modeling is going to work, what you're going to need, whether that feature that you want to build is even modelable in the first place..." *- Craig Martell

ABOUT CRAIG MARTELL

Craig is Head of Lyft Machine Learning. He’s also an adjunct professor of Machine Learning for Northeastern University’s Align program.

Prior to joining Lyft, he was Head of Machine Intelligence @ Dropbox, and led a number of AI teams and initiatives at LinkedIn, including the development of the LinkedIn AI Academy. Before LinkedIn, Craig was a tenured computer science professor at the Naval Postgraduate School specializing in natural language processing (NLP). He has a Ph.D. in Computer Science from the University of Pennsylvania and is the co-author of the MIT Press book Great Principles of Computing.

RESOURCES

• (ML training) Galvanize: https://bit.ly/3ckF6Gz
• (website) Andrew Ng: https://bit.ly/3cgerdU
• (courses) ML: https://bit.ly/3iNB9gf | AI for Everyone: https://bit.ly/2HeZKwl
• (course) Fast.AI: https://bit.ly/35SPwMD
• (book) "Hands-On ML with Scikit" : https://amzn.to/35SbGyh

SHOW NOTES

• An overview of the machine learning lifecycle (2:49)
• The most expensive and time-consuming aspect of the machine learning lifecycle (6:07)
• The key skills of a machine learning team (7:21)
• How do you build an AI/ML Team and what are the different models? (8:41)
• What to do If you’re an engineering manager with no AI/ML skills or experience (15:19)
• How deep does your understanding of AI/ML have to be in order to lead effectively? (18:48)
• How do you estimate project timelines for AI/ML teams? (19:15)
• What are the biggest mistakes engineering leaders make managing AI/ML teams? (20:52)
• How do you manage expectations in an organization that’s in the early days of AI/ML development? (21:33)
• What are sources of technical debt unique to AI/ML systems? (22:13)
• How do machine learning teams interface with product teams? (23:55)
• AI/ML resources for executive engineering leaders (25:44)
• When’s the right time to invest in AI/ML? (26:10)
• Can you apply the Pareto Principle (80/20 rule) to AI/ML development? (26:40)
• Takeaways (28:12)

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