The performance and accuracy of AI and ML systems directly depend on the quality, relevance, and integrity of the data, which are used to either train the model or accessed as part of Retrieval Augmented Generation (RAG) systems for generative AI assistants. Data engineering plays a crucial role in ensuring high-quality data is available, accessible, and usable for successful AI and ML implementations, allowing models to learn, reason, and act effectively. This session explores how AWS analytics provides complete engineering solutions for ingesting, storing, processing, and integrating data and making the data accessible for generative AI chat assistants and other AI and ML applications.
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