Getting started with Amazon SageMaker Canvas | Amazon Web Services
Introduction to SageMaker Canvas
Overview of SageMaker Canvas and its capabilities in a low-code, no-code environment for generative AI.
What is SageMaker Canvas?
- SageMaker Canvas is part of SageMaker Studio, designed for business analysts to interact with language models without coding.
- It provides a fully managed IDE for machine learning, decoupling the UI from various applications.
- Users can utilize JupyterLab or Visual Studio Code within SageMaker Studio for developing and running notebooks.
- Features include Auto ML integration, model evaluation, training jobs, and access to prebuilt models via SageMaker JumpStart.
- The demo showcases deploying a Llama 2 chat model using SageMaker Studio.
Exploring Capabilities in SageMaker Canvas
Detailed exploration of features available in the SageMaker Canvas application.
Interacting with Language Models
- Users can initiate chats with different models like Claude 2 and Llama 2 directly within the application.
- The ability to compare responses from multiple foundation models enhances user experience and insights.
- Integration with Amazon Bedrock allows users to access various models alongside those available through JumpStart.
- Both Claude 2 and Llama 2 provide valuable insights on responsible AI topics such as explainability and fairness.
Enhancing Model Responses with Retrieval-Augmented Generation
Utilizing retrieval mechanisms to improve the quality of responses generated by language models.
Implementing Kendra Indexing
- A retrieval augmented generation stack was added using Amazon Kendra for enhanced query capabilities.
- An index was created from recent research papers uploaded to an S3 bucket, allowing targeted queries about large language models.
- By enabling document querying in Canvas, users can extract relevant information directly linked to their queries.