Jupyter notebooks for SageMaker MLOps, from idea to production
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This repository provides a structured, six-step guide for moving machine learning projects from initial idea to production using Amazon SageMaker. It targets ML engineers and data scientists seeking to implement MLOps best practices, offering a practical, hands-on approach to operationalizing ML workflows.
How It Works
The project guides users through a series of Jupyter notebooks, each building upon the last. It starts with local experimentation and progressively integrates SageMaker's MLOps capabilities, including SageMaker Pipelines for workflow orchestration, Feature Store for managing ML features, Model Registry for versioning, MLflow for experiment tracking, and Model Monitor for performance oversight. This incremental approach allows users to gradually adopt advanced MLOps features.
Quick Start & Requirements
git clone https://github.com/aws-samples/amazon-sagemaker-from-idea-to-production.git
) and execute the 00-start-here.ipynb
notebook within a SageMaker Studio environment.sagemaker-domain.yaml
). Specific IAM policies (AmazonSageMakerFullAccess
, AWSCloudFormationFullAccess
, AWSCodePipeline_FullAccess
, AmazonSageMakerPipelinesIntegrations
, AWSCodeStarFullAccess
) must be attached to the execution role. A public VPC is assumed for the CloudFormation template.Highlighted Details
Maintenance & Community
This repository is part of the aws-samples
organization, indicating official AWS backing. Further community engagement or roadmap details are not explicitly mentioned in the README.
Licensing & Compatibility
Limitations & Caveats
The CloudFormation template for domain creation is limited to us-east-1
, us-west-2
, and eu-central-1
regions. Users must ensure their AWS account has a public VPC configured if using the template.
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