arize-phoenix 0.0.3

ML Observability in your notebook

Phoenix provides MLOps insights at lightning speed with zero-config observability for model drift, performance, and data quality.

Phoenix is under active development. APIs may change at any time.

• Installation
• Getting Started
• Documentation
• Community
• Contributing
• License

Installation

pip install arize-phoenix

Getting Started

In this section, you will get Phoenix up and running with a few lines of code.

After installing arize-phoenix in your Jupyter or Colab environment, open your notebook and run

import phoenix as px

datasets = px.load_example("sentiment_classification_language_drift")
session = px.launch_app(datasets.primary, datasets.reference)

Next, visualize your embeddings and inspect problematic clusters of your production data.

TODO(#297): Include GIF where we navigate to embeddings, zoom in and rotate, and select a cluster.

Don't forget to close the app when you're done.

px.close_app()

For more details, check out the Sentiment Classification Tutorial.

Documentation

For in-depth examples and explanations, read the docs.

Community

Join our community to connect with thousands of machine learning practitioners and ML observability enthusiasts.

• 🌍 Join our Slack community.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
• 🌟 Leave a star on our GitHub.
• 🐞 Report bugs with GitHub Issues.
• 🗺️ Check out our roadmap to see where we're heading next.
• 🎓 Learn the fundamentals of ML observability with our introductory and advanced courses.
• ✏️ Check out our blog. TODO(#291): Add blog filter for Phoenix
• ✉️ Subscribe to our mailing list. TODO(#294): Add link
• 🐦 Follow us on Twitter.
• 👔 Check out our LinkedIn. TODO(#292): Add link, fix badge

Contributing

• 💻 Read our developer's guide.
• 🗣️ Join our Slack community and chat with us in the #phoenix-devs channel.

License

Arize-Phoenix is licensed under the Elastic License 2.0 (ELv2).