Completed
- Overlooked issues and how ML Ops can prevent them
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Classroom Contents
Navigating the MLOps Landscape
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- 1 - Introduction
- 2 - Speaker Introduction
- 3 - Challenges in ML operationalization and ML Ops solutions
- 4 - ML Ops and ultra-large scaling ad auction systems
- 5 - Overlooked issues and how ML Ops can prevent them
- 6 - A story highlighting the importance of ML Ops
- 7 - Concept drift and ML Ops
- 8 - Open source's role in accelerating ML Ops adoption
- 9 - Docker and Kubernetes in ML Ops
- 10 - Best practices for version control in ML Ops
- 11 - Addressing company refusal to adopt open source
- 12 - Security concerns and other issues with open source in ML Ops
- 13 - Closing remarks
