Python SDK
Arize AI for Model Monitoring, Troubleshooting, and Explainability
Use the Arize Python package to monitor machine learning predictions to observe your ML models and their features, predicted labels, and actual labels with just a few lines of code.
Installing the package
In addition to the basic functionality installed by the command above, the Arize SDK has additional functionality that can be installed with some extra dependencies:
LLM Tracing
With this extra module, Arize can receive your traces and spans to break down your system's components into discrete inputs and outputs to help debug your LLM application. Learn more here.
Auto Embeddings
With this extra module, Arize properly extracts the embeddings depending on your use case, and we return it to you to include in your Pandas DataFrame. Learn more here. To install the Arize package including this functionality:
NLP Metrics
With this extra module, Arize helps you calculate evaluation metrics for your NLP Generative tasks. Learn more here. To install the Arize package including this functionality:
Mimic Explainer
With this extra module, Arize allows the user to pass a flag with their request to send data that would produce SHAP values using the surrogate explainability approach. Learn more here. To install the Arize package including this functionality:
Logging Options
The Arize Python SDK offers 2 ways of logging data into the platform:
Pandas Batch Logging
Designed for logging a batch of your model inferences using Pandas DataFrames. Go to the following page for more information.
Single Record Logging
Designed for low latency, one-at-a-time, logging of your model inferences. Go to the following page for more information.
End of Support Table
Changelog
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