End to end ML Ops infrastructure (video)

This is a personal project to demonstrate a fully automated end-to-end Machine Learning infrastructure, including reading data from a feature store, automated model creation with hyperoptimization tuning and automatically finding the most efficient model, storing models in a model registry, building a CI/CD pipeline for deploying the registered model into a production environment, serving the model using HTTP API or Streaming interfaces, implementing monitoring, writing tests and linters, creating regular automated reporting.


Source: https://github.com/razorcd/mlops-project


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Written on September 22, 2022
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