Amazon Q Developer enhances generative AI in Amazon SageMaker Unified Studio, facilitating data discovery, processing, SQL analytics, and ML workflows. Key improvements to the chat experience in the JupyterLab IDE now include command-line functionality and integration with Model Context Protocol (MCP) servers, ensuring personalized and relevant AI assistance for developers. Users can leverage this enhanced tool for efficient setup, code troubleshooting, and refactoring, while maintaining transparency in AI interactions.
This tutorial provides a comprehensive guide to utilizing the Amazon Q Developer chat and CLI for executing ETL tasks and building ML workflows. Prerequisites include access to SageMaker Unified Studio environments and datasets. With the new AI assistance, data engineers and ML engineers can streamline workflows, conduct exploratory analysis, and develop predictive models effectively, utilizing the provided tools to conduct feature engineering and enhance model performance. Explore MCP capabilities further in the SageMaker Unified Studio User Guide to optimize your data engineering and machine learning processes.
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