Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting.
Features
- ML observability in a notebook
- Lightweight connections to dataframes
- Provides easy tools to generate and visualize embeddings
- Automatically find clusters of embeddings that represent "ideas" that the model has learned (manifolds)
- Sorts clusters of issues using performance metrics or drift
- Built in workflows for model improvement
License
MIT LicenseFollow Arize Phoenix
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