bodywork-ml/ml-pipeline-engineering
Best practices for engineering ML pipelines.
This project offers best practices for building robust and reliable machine learning pipelines, specifically for data science teams deploying models to production. It guides you on taking raw training data, typically CSVs in cloud storage like AWS S3, and turning them into versioned, trained models served via a web API. The primary users are MLOps engineers or data scientists responsible for operationalizing ML models.
No commits in the last 6 months.
Use this if you need to build, test, deploy, and schedule ML pipelines that reliably train and serve models, managing data and model versions efficiently.
Not ideal if you are looking for a fully managed ML platform or a solution for experimental, non-production machine learning workflows.
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36
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9
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 20, 2022
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