Amazon SageMaker Documentation
Bringing together AWS machine learning and analytics capabilities, this
next generation of Amazon SageMaker provides an integrated experience for analytics and AI with
unified access to all of your data. Collaborate and build faster with Amazon SageMaker Unified
Studio (preview) using familiar AWS tools for model development, generative AI,
big data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable
generative AI assistant for software development. Access all of your data whether it's stored in
data lakes, data warehouses, third party, or federated sources with Amazon SageMaker Lakehouse.
Get built-in governance with Amazon SageMaker Data and AI Governance to align with your
enterprise security needs.
Amazon SageMaker Unified Studio (preview)
- Learn how administrators manage users and groups and set up resources for Amazon SageMaker Unified Studio (preview).
- Learn how developers can use the data and tools provided in Amazon SageMaker Unified Studio (preview).
Amazon SageMaker AI
- Onboard to the Amazon SageMaker AI role and domain.
- Learn how to automate machine learning from start to finish.
- Learn about machine learning environments that Amazon SageMaker AI offers.
- Learn how to use a human-in-the-loop to help label data more accurately.
- Learn how to prepare data for machine learning.
- Learn how to process data for machine learning.
- Learn how to create, store, and share extracted data signals (features) for machine learning.
- Learn how to use SageMaker training plans to reserve GPU capacity for your large-scale AI model training workloads.
- Learn how to train your machine learning models.
- Learn how to deploy your machine learning models for inference.
- Learn how to implement machine learning operations on Amazon SageMaker AI.
- Learn how to monitor data and model quality.
- Learn how to use Docker containers to build your machine learning models.
Amazon Bedrock IDE integrated in Amazon SageMaker Unified Studio (preview)
- Learn how to use the Amazon Bedrock IDE in Amazon SageMaker Unified Studio (preview).
Responsible AI
Reference
- Use the AWS SDK for Python (Boto3) to format model data and build applications to build, train, and deploy machine learning models.
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