Exploring Data Cleaning and Preprocessing for Machine Learning Applications

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Exploring the essential role of data cleaning and preparation in the RAG pipeline, bridging the gap between raw source documents and the Language Object Model (LOM). The discussion emphasizes the significance of data quality in machine learning and AI, underscoring the complexities and advantages of appropriately preprocessing documents.

The majority of enterprise data exists in heterogenous formats such as HTML, PDF, PNG, and PowerPoint. However, large language models do best when trained with clean, curated data. This presents a major data cleaning challenge.

Unstructured is focused on extracting and transforming complex data to prepare it for vector databases and LLM frameworks.

Crag Wolfe is Head of Engineering and Matt Robinson is Head of Product at Unstructured. They join the podcast to talk about data cleaning in the LLM age.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .