Automated MLOps workflow deploys SageMaker models to Amazon EKS

This article details an automated MLOps workflow for deploying machine learning models from Amazon SageMaker to Amazon EKS. The process involves training an XGBoost model using SageMaker Pipelines, followed by an AWS CodeBuild deployment workflow that containerizes the approved model and deploys it to Amazon EKS. The system emphasizes repeatability and traceability, ensuring that the exact model package used for training is deployed. AI

IMPACT Streamlines the deployment of machine learning models, enabling faster iteration and productionization of AI applications.

RANK_REASON Article describes a technical implementation for MLOps and DevOps workflows using existing cloud services, not a new product release or core research.

Read on Medium — MLOps tag →

• 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

• product
• infra

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