ML Observability in your notebook
Project description
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
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
train_ds, prod_ds = px.load_dataset("sentiment_classification_language_drift")
px.launch_app(train_ds, prod_ds)
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 the Arize 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 the Arize blog. TODO(#291): Add blog filter for Phoenix
- ✉️ Subscribe to the Arize newsletter. TODO(#294): Add link
- 📺 Watch the Arize YouTube channel.
- 🐦 Follow us on Twitter.
- 👔 Check out our LinkedIn. TODO(#292): Add link, fix badge
Contributing
- 💻 Read our developer's guide.
- 🗣️ Join the Arize Slack community and chat with us in the #phoenix-devs channel.
License
Arize-Phoenix is licensed under the Elastic License 2.0 (ELv2).
Project details
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