Amazon SageMaker AI provides the capabilities to build, train, and deploy machine learning and foundation models using fully managed infrastructure, tools, and workflows. Amazon SageMaker Lakehouse unifies data access across multiple data sources, including Amazon S3 data lakes and Amazon Redshift. Therefore, the combination of these two capabilities perfectly matches the user requirements.
Amazon SageMaker Data and AI Governance enables organizations to securely discover, govern, and collaborate on data and AI using Amazon SageMaker Catalog, which is built on Amazon DataZone.
Amazon SageMaker AI is a fully managed machine learning service that enables developers to build, train, and deploy ML models into a production-ready hosted environment. It provides managed ML algorithms designed to run efficiently against extremely large datasets in distributed environments, and supports bring-your-own-algorithms and frameworks. Amazon Bedrock, while part of the SageMaker ecosystem, is specifically used to build and scale generative AI applications.
According to the AWS documentation, Amazon SageMaker AI supports building, training, and deploying foundation models with fully managed infrastructure, tools, and workflows. Amazon Bedrock is a capability within the Amazon SageMaker ecosystem used specifically to build and scale generative AI applications.
Amazon Bedrock is a fully managed service that provides secure, enterprise-grade access to over 100 foundation models from leading industry providers (such as Amazon, Anthropic, DeepSeek, Moonshot AI, MiniMax, and OpenAI) to build and scale generative AI applications. It also provides options to customize these models to enhance performance and quality for specific use cases.
Amazon Bedrock supports more than 100 foundation models from leading industry providers, including Amazon, Anthropic, DeepSeek, Moonshot AI, MiniMax, and OpenAI. It also provides options to customize these models to improve performance and quality for specific use cases.
Amazon SageMaker Data and AI Governance is designed specifically to let users discover, govern, and collaborate on data and AI securely with Amazon SageMaker Catalog, which is built on Amazon DataZone.
Amazon SageMaker Data Processing enables analyzing, preparing, and integrating data for analytics and AI using open-source frameworks on Amazon Athena, Amazon EMR, and AWS Glue.
According to the AWS documentation, Amazon Bedrock is the service capability designed to build and scale generative AI applications, while Amazon SageMaker AI is a fully managed machine learning service that enables data scientists and developers to quickly build, train, and deploy machine learning models in a hosted environment.
Amazon SageMaker Data and AI Governance is the specific capability built on Amazon DataZone that utilizes Amazon SageMaker Catalog to securely discover, govern, and collaborate on data and AI.
Amazon SageMaker Data and AI Governance enables users to discover, govern, and collaborate on data and AI securely using Amazon SageMaker Catalog, which is built on Amazon DataZone. This directly addresses the requirement for secure cataloging and governance built on Amazon DataZone.
Amazon SageMaker Unified Studio allows building with data and tools for analytics and AI inside a single development environment, which satisfies the team's requirement to build with all data and tools in a single environment.
Amazon SageMaker AI is a fully managed machine learning (ML) service that enables data scientists and developers to build, train, and deploy ML and foundation models into a production-ready hosted environment using fully managed infrastructure, tools, and workflows.
Amazon Bedrock is a fully managed service that provides secure, enterprise-grade access to more than 100 high-performing foundation models from industry-leading providers such as Amazon, Anthropic, DeepSeek, and others. Furthermore, Bedrock supports customizing these models to improve performance and quality for specific use cases, making it the perfect fit for the organization's requirements.
An organization is planning to build, train, and deploy machine learning and foundation models using fully managed infrastructure, tools, and workflows. Additionally, they need to unify data access across multiple data sources, including their existing Amazon S3 data lakes and Amazon Redshift. Which combination of Amazon SageMaker capabilities best meets these requirements?
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