December 2022
Beginner to advanced
2h 50m
English
conference
AI Superstream: MLOps
by Shingai Manjengwa, Susan Shu Chang, Olga Tsubiks, Noah Gift, Jason Bell, Isabel Zimmerman, Todd Underwood
Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)
Overview
MLOps is consistently one of the greatest challenges engineers face when creating and maintaining machine learning systems. Join expert practitioners to learn techniques and best practices for operationalizing machine learning models and explore case studies of them in action, showing you what works—and what doesn't.
What you’ll learn and how you can apply it- Understand MLOps processes for model deployment, containerization, and automation as well as monitoring, continuous experimentation, and improvement
- Learn how an understanding of SRE and DevOps principles can enhance the practice of MLOps
- Avoid common pitfalls in the process of building end-to-end machine learning pipelines
- You're a data or machine learning practitioner who puts machine learning models into production, or you’re embarking on an MLOps career path.
- You want to improve your process of productionizing machine learning models by applying new techniques and best practices.
Recommended follow-up:
- Read Practical MLOps (book)
- Read Reliable Machine Learning (book)
- Watch Radar Talks: Hugo Bowne-Anderson on MLOps Versus DevOps (video)
- Take Practical MLOps (live online course with Noah Gift)
- Take Open Source MLOps in 4 Weeks (live online course with Alex Kim)
- Read Site Reliability Engineering (book)
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