aws-samples/amazon-sagemaker-pipeline-deploy-manage-100x-models-python-cdk
This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.
This project helps MLOps engineers and data scientists efficiently deploy and manage a large number of machine learning models. It takes raw training data (like customer churn information) and automatically processes, trains, tunes, and deploys multiple models, each tailored to specific data segments. The output is a set of live, continuously updated machine learning models ready for real-time predictions.
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Use this if you need to automate the end-to-end lifecycle for hundreds of machine learning models, from data ingestion to deployment and ongoing updates, without manual intervention.
Not ideal if you only manage a small number of models or require extremely custom, non-standard deployment logic for each individual model that cannot be templated.
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Python
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MIT-0
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Jul 14, 2025
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