cover of episode Understanding Machine Learning Features and Platforms

Understanding Machine Learning Features and Platforms

2023/8/16
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The Cloudcast

Shownotes Transcript

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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