Build an Event Driven Machine Learning Pipeline on Kubernetes
summary
Shows how to assemble an end-to-end, event-driven ML pipeline on Kubernetes using Kubernetes build, eventing, serving and pipeline components and CRDs. Covers automating detection of events such as data drift, bias or accuracy loss, triggering notifications and actions, building and running data scientists’ code from source, and supporting A/B testing, dark launches, training, validation, serving and operations.
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