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[] Prerequisites
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Classroom Contents
The Motivation for MLOps - Architectural Perspective
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- 1 [] Introduction to Steven Fines
- 2 [] Highlight
- 3 [] Motivating to MLOps
- 4 [] Machine Learning in the wild
- 5 [] Compliance challenges
- 6 [] Model Management
- 7 [] Operational concerns
- 8 [] MLOps: Definition from the Architectural viewpoint
- 9 [] MLOps and DevOps
- 10 [] Do I need to do this?
- 11 [] What is the framework?
- 12 [] Prerequisites
- 13 [] Foundational Components
- 14 [] Scaling the process
- 15 [] Fully mature
- 16 [] Implementing MLOps
- 17 [] Standardize
- 18 [] Move model execution to pipelines
- 19 [] Monitor the results
- 20 [] Scales Steven worked with before
- 21 [] When to create a data catalog or a feature store
- 22 [] Data catalogueing
- 23 [] Classic obstacles in ML that doesn't present in classical or traditional software
- 24 [] Wrap up
