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
Quickstart
Import libraries.
from dataclasses import replace
import pandas as pd
import phoenix as px
Download curated datasets and load them into pandas DataFrames.
train_df = pd.read_parquet(
"https://storage.googleapis.com/arize-assets/phoenix/datasets/unstructured/cv/human-actions/human_actions_training.parquet"
)
prod_df = pd.read_parquet(
"https://storage.googleapis.com/arize-assets/phoenix/datasets/unstructured/cv/human-actions/human_actions_production.parquet"
)
Define schemas that tell Phoenix which columns of your DataFrames correspond to features, predictions, actuals (i.e., ground truth), embeddings, etc.
train_schema = px.Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
prediction_label_column_name="predicted_action",
actual_label_column_name="actual_action",
embedding_feature_column_names={
"image_embedding": px.EmbeddingColumnNames(
vector_column_name="image_vector",
link_to_data_column_name="url",
),
},
)
prod_schema = replace(train_schema, actual_label_column_name=None)
Define your production and training datasets.
prod_ds = px.Dataset(prod_df, prod_schema)
train_ds = px.Dataset(train_df, train_schema)
Launch the app.
session = px.launch_app(prod_ds, train_ds)
You can open Phoenix by copying and pasting the output of session.url
into a new browser tab.
session.url
Alternatively, you can open the Phoenix UI in your notebook with
session.view()
When you're done, don't forget to close the app.
px.close_app()
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.
Thanks
- UMAP For unlocking the ability to visualize and reason about embeddings
- HDBSCAN For providing a clustering algorithm to aid in the discovery of drift and performance degradation
License
Arize-Phoenix is licensed under the Elastic License 2.0 (ELv2).
Project details
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