arize-phoenix 0.0.11rc3

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

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

Copyright and License

Copyright 2023 Arize AI, Inc. All Rights Reserved.

This software is licensed under the terms of the Elastic License 2.0 (ELv2)