MLOps.community Machine Learning Design Patterns // Sara Robinson // MLOps Coffee Sessions #24
Coffee Sessions #24 with Sara Robinson of Google, Machine Learning Design Patterns, co-hosted by Vishnu Rachakonda.
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//Bio
Sara is a Developer Advocate for Google Cloud, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Before Google, she was a Developer Advocate on the Firebase team. Sara has a Bachelor’s degree from Brandeis University. When she’s not writing code, she can be found on a spin bike or eating frosting.
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Timestamps:
[00:00] Introduction to Sara Robinson
[01:38] Sara's Background in Tech
[04:54] What were some things that jumped out at you right away with Machine Learning that are different?
[07:44] Sara's Transition to the Machine Learning realm.
[08:36] What is the role of a Developer Advocate?
[11:41] Compared to traditional software developer advocacy, what stands out to you as being different, unique, perhaps more fun about working in the Machine Learning realm as a Developer Advocate?
[13:40] "No one person has it right."
[15:27] Given how new this space is, how did you go about writing a book? What led you to write this book (Machine Learning Design Patterns)? [19:00] Process of deciding to write the book
[21:46] What is it that made the focus of these design patterns?
[25:07] Who's the reader that you think who's gonna have this book on their shelf as a reference?
[26:42] How would you advise readers to go about reconciling these domain-based needs and the design patterns that you may suggest or identify? [31:20] Can you tell us about a time that some of the design patterns, as you're learning with your co-authors, have been useful to you?
[36:50] Workflow Pipeline breakdown in the book
[42:23] How do you think about that level of maturity in terms of thinking about the design patterns?
[46:06] How do I communicate in a design pattern? What if there is resistance to formalization or implementational structure because it might prevent creativity or reiteration?
[49:32] Pre-bill and custom components of Pipeline Frameworks
[51:28] How do we know to do the next step or stay in Feature Store patterns? [56:07] Are we going to see the convergence of tools and frameworks soon?
Resources referenced in this episode:
https://www.oreilly.com/library/view/machine-learning-design/9781098115777/
https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783 https://books.google.com.ph/books/about/Machine_Learning_Design_Patterns.html?id=djwDEAAAQBAJ&redir_esc=y
https://amzn.to/38tM22C
https://sararobinson.dev/2020/11/17/writing-a-technical-book.html