Machine Learning Design Patterns for MLOps // Valliappa Lakshmanan // MLOps Meetup #49 from Agentic Conversations (formally mlops.community)

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Machine Learning Design Patterns for MLOps // Valliappa Lakshmanan // MLOps Meetup #49

56 minutes Posted Feb 2, 2021 at 1:34 pm.

] TWIML Con Debate announcement to be hosted by Demetrios on Friday

] Should data scientists know about Kubernetes? Is it just one machine learning tool to rule them all? Or is it going to be the "best-in-class" tool?

] Strong opinion of Lak about "Should data scientists know about Kubernetes?"

] Lak's background into tech

] Which ones you wrote in the book? Is the airport scenario yours?

] Did you write ML Maturity Level from Google?

] How do you know when to bring on perplexity for the sake of making things easier?

] What are some of the best practices that you've seen being used in tooling?  

] How did you come up with writing the book?

] How did we decide that these are the patterns that we need to put in the book?

] Why did I get the "audacity" to think that this is something that is worth doing?

] What would be in your mind some of the hierarchy of design patterns?

] Are there patterns out there that are yet to be discovered? How do you balance the exploitable vs the explorable ml patterns?

] ModelOps vs MLOps

] Do you feel that a DevOps engineer is better suited to make the transition into becoming a Machine Learning engineer?

] Fundamental Machine Design Patterns vs Software Development Design Patterns

] When you're working with the companies at Google, did you give them a toolchain and a better infrastructure or was there more to it? Did they have to rethink their corporate culture because DevOps is often mistaken as just a pure toolchain?

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MLOps community meetup #49! Last Wednesday we talked to Lak Lakshmanan, Data Analytics and AI Solutions, Google Cloud.

// Abstract:

Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalogue tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects.

Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these, and other, design patterns.

// Bio:

Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

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Connect with Lak on LinkedIn: https://www.linkedin.com/in/valliappalakshmanan/

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