aws-samples/sample-aiops-on-amazon-sagemakerai

A collection of examples and resources for operationalizing GenAI and ML workloads on Amazon SageMakerAI with integrated SageMaker-managed MLflow and Amazon Bedrock.

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Emerging

This project helps ML administrators, platform engineers, and data scientists effectively manage and operationalize their Generative AI and Machine Learning models. It provides examples, scripts, and configurations for tasks like monitoring model performance, migrating MLflow data, and tracking resource usage. Users can gain insights into model experiments, deployments, and overall MLOps workflows.

Use this if you are a Machine Learning Administrator, Data Scientist, or ML Engineer looking to streamline the operational management of your AI and ML models, especially when using Amazon SageMaker and MLflow.

Not ideal if you are an end-user without technical expertise in MLOps, machine learning, or cloud platforms like Amazon SageMaker.

MLOps GenAI Operations Model Monitoring ML Platform Administration Data Science Workflow
No Package No Dependents
Maintenance 10 / 25
Adoption 4 / 25
Maturity 15 / 25
Community 17 / 25

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Stars

8

Forks

10

Language

Jupyter Notebook

License

MIT-0

Last pushed

Feb 23, 2026

Commits (30d)

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