This episode currently has no reviews.
Submit ReviewLuigi is the director of data science at 2U, where he leads a team in developing ML models and infrastructure to predict student success outcomes. He's also the founder of ML in Production, a blog and newsletter that helps readers build, deploy, and run ML systems.
Learn more about Luigi:
https://twitter.com/mlinproduction
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletter
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/
Timestamps:
02:45 Luigi Patruno
04:50 How can ML teams be more rigorous in their engineering practices?
10:25 Best practices for monitoring and logging ML systems
18:00 Adding business value with data science
37:10 Most valuable types of tools for ML in production
43:15 What an ideal data pipeline setup looks like
47:50 Unbundling the "Data Scientist" role
50:35 The future of building software: "Code 2.0"
59:45 Most valuable skills for the future
01:10:15 Learnings from writing his blog "ML in Production"
01:15:00 Rapid fire questions
Links:
Ultimate Guide to Deploying ML Models
Maximizing Business Impact with Machine Learning
Two Types of Companies Using ML
Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production
Machine Learning is Forcing Software Development to Evolve
ML Street Talk #29: GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence
Luigi is the director of data science at 2U, where he leads a team in developing ML models and infrastructure to predict student success outcomes. He's also the founder of ML in Production, a blog and newsletter that helps readers build, deploy, and run ML systems.
Learn more about Luigi:
https://twitter.com/mlinproduction
Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletter
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Subscribe to ML Engineered: https://mlengineered.com/listen
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/
Timestamps:
02:45 Luigi Patruno
04:50 How can ML teams be more rigorous in their engineering practices?
10:25 Best practices for monitoring and logging ML systems
18:00 Adding business value with data science
37:10 Most valuable types of tools for ML in production
43:15 What an ideal data pipeline setup looks like
47:50 Unbundling the "Data Scientist" role
50:35 The future of building software: "Code 2.0"
59:45 Most valuable skills for the future
01:10:15 Learnings from writing his blog "ML in Production"
01:15:00 Rapid fire questions
Links:
Ultimate Guide to Deploying ML Models
Maximizing Business Impact with Machine Learning
Two Types of Companies Using ML
Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production
Machine Learning is Forcing Software Development to Evolve
ML Street Talk #29: GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence
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