This article details an automated MLOps workflow for moving machine learning models from training to production on Amazon EKS. It utilizes Amazon SageMaker for model tuning, evaluation, and explainability, followed by AWS CodeBuild and Amazon ECR for containerization and deployment. The process emphasizes repeatability, ensuring traceable model artifacts and approved packages are used for deployment. AI
IMPACT Streamlines the deployment of ML models into production environments, enhancing MLOps efficiency and repeatability.
RANK_REASON The article describes a technical workflow for deploying ML models using existing cloud services, rather than a new product release or research.
- Amazon EKS
- Amazon Elastic Container Registry
- Amazon EventBridge
- Amazon S3
- Amazon SageMaker
- AWS CodeBuild
- GitHub
- SageMaker Clarify
- SageMaker Model Registry
- SageMaker Python Software Development Kit (SDK)
- XGBoost
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