Monitoring models for drift and degradation is not easy - theoretically or practically. We've teamed-up with Aporia to demonstrate how their platform can solve these problems for you, by using their Python client from within a Bodywork pipeline.Continue reading →
Second in a series of articles demonstrating how to engineer and deploy a ML pipeline. This part focuses on developing the pipeline: automating model-training, serving the latest model, handling errors, writing tests and reusing code across projects.Continue reading →
First in a series of articles demonstrating how to engineer a ML pipeline and deploy it to a production environment. This part covers: solution architecture, project structure, setting-up automated tests, making an initial deployment and configuring a CI/CD.Continue reading →
Bodywork is flexible enough to deploy almost any type of Python project to Kubernetes. We demonstrate this by using it to deploy a production-ready instance of MLflow, then show how MLflow can be used alongside Bodywork's ML deployment capabilities, to form a powerful open-source MLOps stack.Continue reading →
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