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Amazon Sage Maker AI

Amazon SageMaker  is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train, and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more all in one integrated development environment (IDE). SageMaker supports governance requirements with simplified access control and transparency over your ML projects.

Amazon Sage Maker Ai Features

• Store, share, and manage features for machine learning models.

• Aggregate and prepare tabular and image data for machine learning in minutes.

• Access geospatial, purpose-built processing operations, pretrained models, and built-in visualisation tools for geospatial ML.

• Explore and build machine learning models with fully managed notebooks.

• A machine learning hub with foundation models, algorithms, and solutions deployable with just a few clicks

• Reduce the time and cost to train and tune models at scale without managing infrastructure.

• Efficiently manage machine learning experiments.

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Amazon Sage Maker AI

Amazon SageMaker  is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train, and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more all in one integrated development environment (IDE). SageMaker supports governance requirements with simplified access control and transparency over your ML projects.

Amazon Sage Maker Ai Features

• Store, share, and manage features for machine learning models.

• Aggregate and prepare tabular and image data for machine learning in minutes.

• Access geospatial, purpose-built processing operations, pretrained models, and built-in visualisation tools for geospatial ML.

• Explore and build machine learning models with fully managed notebooks.

• A machine learning hub with foundation models, algorithms, and solutions deployable with just a few clicks

• Reduce the time and cost to train and tune models at scale without managing infrastructure.

• Efficiently manage machine learning experiments.

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