How to Containerize Your MLOps Pipeline from Training to Serving

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How to Containerize Your MLOps Pipeline from Training to Serving
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Last year, our ML team shipped a fraud detection model that worked perfectly in a Jupyter notebook. Precision was excellent. Recall numbers looked great. Everyone was excited – until we tried to deploy it. The model depended on a specific version of scikit-learn that conflicted with the production Python environment...

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