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Calling Aporia from Bodywork Pipelines to Monitor Models in Production

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.

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Engineering ML Pipelines - Part 2

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.

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Engineering ML Pipelines - Part 1

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.

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CI/CD for Machine Learning

A guide toΒ CICD for ML, together with a template repository to get you started.

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bodywork-core v2.1.0 has been released

bodywork-core v2.1.0 is available to download from PyPI.

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Serving Uncertainty

Handling prediction uncertainty in production systems using Bayesian inference and probabilistic programs.

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Deploy MLflow with Bodywork

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.

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Getting Started with Kubernetes for Machine Learning Deployment

Get up-and-running with Kubernetes in under 10 minutes from a standing-start.

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