Scale machine learning across your
business with MLOps
MLOps practices help you streamline the ML lifecycle by automating and standardizing
ML workflows. With standardized MLOps processes in place, your teams can get models
into production faster and collaborate more effectively. Over time, MLOps can help you
reach your ultimate goal—scaling ML adoption and using ML to improve results across the
entire organization.
Amazon SageMaker delivers the capabilities, automation, standardization, and
centralization you need to make MLOps a reality for your organization. Purpose-built
MLOps tools within SageMaker allow you to easily train, test, troubleshoot, deploy, and
govern ML models at scale. This helps improve the productivity of your data scientists and
ML engineers while maintaining model performance in production.
With the purpose-built MLOps tools provided by SageMaker, you can:
• Create repeatable training workflows to accelerate model development
• Catalog ML artifacts centrally for model reproducibility and governance
• Integrate ML workflows with continuous integration and continuous delivery (CI/CD)
pipelines for faster time to production
• Continuously monitor data and models in production to maintain quality
Learn more about SageMaker for MLOps ›
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