Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help you automate and standardize processes across the ML lifecycle. Using SageMaker MLOps tools, you can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production.

Create repeatable training workflows to accelerate model development. Catalogue ML artifacts centrally for model reproducibility and governance. Integrate ML workflows with CI/CD pipelines for faster time to production. Continuously monitor data and models in production to maintain quality. automate and standardize processes across the ML lifecycle using purpose-built tools for machine learning operations (MLOps). Easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production.

Use SageMaker for MLOps solution to orchestrate and manage:

• Building custom images for processing, training, and inference
• Data preparation and feature engineering
• Training models
• Evaluating models
• Deploying models
• Monitoring and updating models