Build an Event Driven Machine Learning Pipeline on Kubernetes
summary
Shows how to construct an end-to-end, event-driven ML pipeline on Kubernetes using Kubernetes build, eventing, serving and pipeline CRDs. Covers automated detection of operational events (data drift, bias, accuracy loss), notifications and automated actions, plus building and running data scientist code from source, A/B testing, dark launches, and orchestration of model training, validation, serving and operations.
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chapters (4)
- 0:00Start
- 5:23Noural Network Design Workflow
- 6:58Neural Network Modeller within Watson Studio
- 11:13ART Demo
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