Continuous deployment of Machine Learning models
summary
The talk examines how to apply Continuous Delivery practices to machine learning models in Data Lake–based projects, addressing model evolution and whether deployment standards and automated monitoring can be adapted. It presents different approaches for reconciling Continuous Delivery workflows with the specific needs of ML model deployment and lifecycle management.
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chapters (7)
- 0:00Introduction
- 1:25ML project specifics
- 4:12Classic lifecycle limits
- 8:44Continuous ML deployment
- 10:09ML Platform concepts
- 13:37Automation scale next steps
- 15:13Q&A
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