HyperLens

HyperLens: Open-Source Machine Learning Model Observability Tool

HyperLens helps data scientists debug, monitor, and optimize machine learning models during development and in production. Key features include model monitoring, data monitoring, explainability analysis, and robustness testing.

What is HyperLens?

HyperLens is an open-source machine learning model observability platform that helps data scientists debug, monitor, and optimize machine learning models during development and in production. It provides several key capabilities:

• Model monitoring - Track model performance metrics like accuracy, Data drift,explainability over time to detect issues.
• Data monitoring - Explore and analyze the incoming data to your models in real-time to check for outliers,drifts or other anomalies.
• Explainability analysis - Generate explanations like feature importance, partial dependence plots to increase trust and transparency into model predictions.
• Robustness testing - Check model behavior with edge cases and stress testing to prevent unexpected failures.

By leveraging HyperLens, data scientists can accelerate development with faster debugging cycles, reduce risk of failures through continuous monitoring, and explain models better. It supports all major ML frameworks like PyTorch, TensorFlow, scikit-learn, and can be easily deployed into any environment.

HyperLens Features

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