ML System Design

Housekeeping

Offers Machine Learning Systems Design(MLOps)πŸ₯‡.
🏒 cs329s Official Page:
πŸ“Ή Video Lectures:
πŸ“‘ : Designing Machine Learning Systems Book by
πŸ”Έ Machine learning systems design is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified requirements.
πŸ”Έ The tutorial approach has been tremendously successful in getting models off the ground. However, the resulting systems tend to go outdated quickly because
(1) the tooling space is being innovated,
(2) business requirements change, and
(3) data distributions constantly shift.
Without an intentional design to hold all the components together.
πŸ“… Catalog of Lectures :
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πŸ”Ή 1. Understanding machine learning production:
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πŸ”Ή 2. ML and Data Systems Fundamentals:
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πŸ”Ή 3. Training Data:
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πŸ”Ή 4. Feature engineering:
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πŸ”Ή 5. Model selection, development, and training:
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πŸ”Ή 6. Offline evaluation:
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πŸ”Ή 7. Model evaluation Tutorial by Goku Mohandas (MadeWithML):
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πŸ”Ή 8. RecList by our very own Chloe He:
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πŸ”Ή 9. Deployment:
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πŸ”Ή 10. Deployment tutorials:
πŸ“‘: How to evaluate MLOps tools by Hamel Husain:
, Video :
πŸ“‘: Deploy models with Ray Serve by Simon Mo (Anyscale):
πŸ”Ή 11. Diagnosis of ML system failures & data distribution shifts & monitoring:
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πŸ”Ή 12. Monitoring & Continual Learning Data Distribution Shifts on Streaming Data by Shreya Shankar:
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πŸ”Ή 13. Model Deployment @ Stitch Fix by Stefan Krawczyk:
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πŸ”Ή 14. Experiment tracking & versioning with Weights & Biases by Lavanya Shukla:
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πŸ”Ή 15. Monitoring Tutorial:
πŸ“‘: WhyLogs tutorials by Alessya Visnjic:
πŸ“‘: Evidently tutorials by Emeli Dral:
πŸ”Ή 16. Deploying time series forecasting and graph neural networks by Kyle Kranen
πŸ“‘: ML beyond accuracy: Fairness, Security, Governance by Sara Hooker :
πŸ”Ή 17. ML Infrastructure and Platform:
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πŸ”Ή 18. Integrating ML into business:
πŸ“‘: Grace Isford (Lux Capital): How to pitch
πŸ“‘: Nnamdi Iregbulem (Lightspeed): Go to market
πŸ“‘: Astasia Myers (Quiet): Business values of AI

Understanding machine learning production


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