Search Results for "ml"

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.

...This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box.

Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase....

This is a Java implementation of WhyLogs, with support for Apache Spark integration for large scale datasets. Understanding the properties of data as it moves through applications is essential to keeping your ML/AI pipeline stable and improving your user experience, whether your pipeline is built for production or experimentation. WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log files that can be used for monitoring, alerts, analytics, and error analysis. ...