CambioML Introduction
CambioML is an open-source machine learning infrastructure company that provides tools for accurate, private, and configurable document retrieval and data extraction using LLMs.
View MoreWhat is CambioML
CambioML, founded in 2023 by Rachel Hu and based in San Jose, CA, is a startup specializing in open-source machine learning infrastructure. The company offers tools and libraries like Uniflow and Pykoi that streamline the process of extracting, transforming, and analyzing data from unstructured sources such as PDFs, HTML, and forms. CambioML aims to bridge the gap between ML development and production, providing a unified interface for data scientists and practitioners to efficiently handle large-scale machine learning projects.
How does CambioML work?
CambioML's technology leverages Large Language Models (LLMs) to extract and transform data from various unstructured sources. Their Uniflow library allows for accurate text extraction from documents like PDFs and HTMLs, with features for data clustering and transformation into desired formats. The Pykoi library facilitates active learning, enabling users to collect labeling demonstration data, train Reinforcement Learning from Human Feedback (RLHF) models, and compare different models. CambioML's tools are designed to handle multi-modality data, offering features like automatic redaction of confidential information and mapping to specific schemas as needed.
Benefits of CambioML
Using CambioML's tools provides several advantages for data scientists and organizations. It significantly reduces the time spent on data cleaning and preparation, which traditionally consumes up to 50% of a data scientist's time. The technology offers higher accuracy in data extraction compared to traditional OCR-based models, with a reported 90% lower error rate. CambioML's solutions also prioritize data privacy, allowing for on-premise deployment and confidential information redaction. The tools' ability to extract insights from proprietary data with ease, coupled with their open-source nature, makes them valuable for both research and enterprise applications, enabling faster R&D and more efficient handling of large-scale document management tasks.
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