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A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
MLU is a modular ML toolkit resembling lodash, streamlining from data prep to deployment with chainable utility functions. It enhances ML workflows, seamlessly integrates with top frameworks, and supports efficient data handling and model evaluation. Open-source, MLU welcomes contributions to foster innovation and efficiency in the ML community.
A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.