Automated ML, no-code, or low-code (original) (raw)

Amazon SageMaker AI offers the following features to automate key machine learning tasks and use no-code or low-code solutions.

• Amazon SageMaker Canvas: For a UI-based, no-code AutoML experience, new users should use the Amazon SageMaker Canvas application in Amazon SageMaker Studio.
  Amazon SageMaker Canvas provides analysts and citizen data scientists no-code capabilities for tasks such as data preparation, feature engineering, algorithm selection, training and tuning, inference, and more. Users can leverage built-in visualizations and what-if analysis to explore their data and different scenarios, with automated predictions enabling them to easily productionize their models. SageMaker Canvas supports a variety of use cases, including computer vision, demand forecasting, intelligent search, and generative AI.
• Amazon SageMaker Autopilot: Amazon SageMaker Autopilot is an automated machine learning (AutoML) feature-set that automates the end-to-end process of building, training, tuning, and deploying machine learning models. Amazon SageMaker Autopilot analyzes your data, selects algorithms suitable for your problem type, preprocesses the data to prepare it for training, handles automatic model training, and performs hyperparameter optimization to find the best performing model for your dataset.
  • As of November 30, 2023, the user interface (UI) for Autopilot is integrated into the Amazon SageMaker Canvas application in Studio.
  • Users of Amazon SageMaker Studio Classic, the previous experience of Studio, can continue using the Autopilot UI in Studio Classic. Users with coding experience can continue using the AutoML API references in any supported SDK for technical implementation.
• Amazon SageMaker JumpStart: SageMaker JumpStart provides pretrained, open-source models for a wide range of problem types to help you get started with machine learning. You can incrementally train and tune these models before deployment. JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for machine learning with SageMaker AI.

Topics

• SageMaker Autopilot
• SageMaker JumpStart pretrained models

Supported Regions and Quotas

SageMaker Autopilot

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