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Amazon SageMaker AI

Amazon SageMaker AI is a fully managed service that provides developers, data scientists with the ability to collect & prepare, build, train and deploy machine learning models quickly.

Amazon SageMaker AI removes heavy lifting from each steps of machine learning process to make it easier to develop high quality models.

Amazon SageMaker AI provides all of the components used for machine learning in a single tool set, so models get to production faster with much less effort & at a lower cost.

Benefits

Make ML more accessible  With Amazon SageMaker AI, machine learning becomes more accessible, enabling people to innovate with ML through a choice of tools, IDE and no code visual interfaces.

Prepare Data at Scale  With Amazon SageMaker AI, you can access, label, and process large amounts of structured data like tabular data from spreadsheets and unstructured data like photos, video and audio.

Accelerate ML Development  With Amazon SageMaker AI, you can reduce training time from hours to minutes with an optimized infrastructure and you can boost productivity with purpose built tools.

Amazon SageMaker AI also enables you to automate and standardize ML Ops practices across your organization, allowing you to train and deploy models at scale.

Use Cases

Amazon Sagemaker AI Canvas  With Amazon SageMaker AI Canvas, business analysts can easily prepare data, train models, and generate predictions using a point and click interface.  They can then share the results with data scientists and integrate them into common business intelligence tools.

Amazon SageMaker AI Studio Notebook  With Amazon SageMaker AI Studio Notebook, data scientists can access from structured and unstructured data sources, improve productivity with purpose built tools, and fully managed Jupiter notebooks with just a few clicks.

Amazon SageMaker AI ML Ops  With Amazon SageMaker AI ML Ops, software developers can build CICD pipelines to reduce model management overhead, automate ML workflows to accelerate data preparation and model building, training and experiments.  They can also monitor ML Model quality by automatically detecting bias and drift and automatically track code data sets and artifacts at each step of the ML life cycle.

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