Скачать с ютуб Machine Learning Engineering in Action // Ben Wilson // Reading Group #5 в хорошем качестве

Machine Learning Engineering in Action // Ben Wilson // Reading Group #5

MLOps Reading Group meeting Reading Group Session about Ben Wilson's Machine Learning Engineering in Action book. https://www.manning.com/books/machine... https://www.amazon.com/Machine-Learni... -------------- ✌️Connect With Us ✌️ ------------ Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Connect with us on LinkedIn:   / mlopscommunity   Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/ Timestamps: [00:00] Ben’s intro [01:22] Machine Learning Engineering in Action book overview [02:19] Chapter 1: What are the different steps of the ML Engineering Roadmap? [13:20] Chapter 2: in this chapter, there is a very interesting list on adapting Agile Manifesto principles to ML project work. From the ML list, which principles have been more impactful in your experience? [18:20] Chapter 9: Test-Driven Development (TDD) for Machine Learning [28:45] Chapter 9: Refactoring a wall-of-text by decomposing it into smaller components that you can actually test [32:00] Chapter 14: What are your thoughts on this matter to better wireframe our ML projects? [36:04] Chapter 16: When implementing feature engineering code for a feature store, how important is it to create good abstractions in terms of possible operations for given features? Or should we just let the different Data scientists upload their feature engineering code instead without paying close attention to abstracting behavior across the feature store? Is there a better middle ground among these two? [44:04] Chapter 13: When do we know that we are going too far and start doing too over-engineered coding solutions? [49:35] How does the code review should be for ML systems? [52:00] Wrap-up