As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally. This combination of ML and operations (MLOps) is helping companies streamline their end-to-end ML lifecycle and boost productivity of data scientists while maintaining high model accuracy and enhancing security and compliance.

In this post, you learn about the key phases of building an MLOps foundations, how multiple personas work together on this foundation, and the Amazon SageMaker purpose-built tools and built-in integrations with other AWS services that can accelerate the adoption of ML across an enterprise business.
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