Understanding Machine Learning Features and Platforms

Gaetan Castelein (@gaetcast, VP Marketing at @tectonai) talks about the complexities of building AI models, features and deploying AI into production for real-time applications. 

SHOW: 745

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SHOW NOTES:

• Tecton (homepage)
• State of Applied Machine Learning 2023 Report
• Hello Fresh adopts Tecton - Good article on features and feature stores
• What is real-time machine learning?
• Feature Platform vs. Feature Store

Topic 1 - Welcome to the show. Tell us a little bit about your background

Topic 2 - Let’s start with some terminology. A lot of our listeners might be relatively new to Machine Learning. I’m still coming up to speed and I actually spent more time than usual just wrapping my head around the concepts and terms and piecing them all together. What is a feature? Why is it important? How many features does ChatGPT 3 have or ChatGPT4?

Topic 3 - How is a feature different from a model? Both are needed, why?

Topic 4 - I’ve always wondered exactly what a data scientist does. Is this where the term Feature Engineering comes into play? Who turns the data into features and picks the appropriate model? 

Topic 5 - Early Machine Learning was analytical ML (offline/batch), correct? How is that different from operational ML (online/batch) and real-time ML?

Topic 6 - Now that we have all that out of the way. What is a Feature Platform? How does it integrate into an organization’s existing Devops workflows and/or CI/CD pipelines? (Features as Code) How is it different from a Feature Store?

Topic 7 - How do you know if the features + model yield a good result? How is prediction accuracy typically measured?

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