---
id: azureml-ai-monitoring
version: "1.0.0"
license: MIT License
license_treatment: permissive
maintenance: dormant
---

# azureml-ai-monitoring — Microsoft Azure Machine Learning Python SDK v2 for collecting model data during operationalization

License: permissive · Maintenance: dormant · Downloads: 102.3K/mo

## What it is and what it does

azureml-ai-monitoring is Microsoft's SDK for instrumenting Azure Machine Learning scoring scripts to collect model data during inference. It provides a Collector class that wraps pandas DataFrames and logs them to configured sinks (typically cloud storage) before, during, and after model predictions, enabling post-deployment monitoring and debugging of model behavior.

The package is designed for use in score.py files deployed to Azure ML endpoints. It correlates input and output data using context tokens, allowing you to trace data transformations and model predictions back to their source requests. It depends on pandas for DataFrame handling and requests for network communication, and supports configurable error handlers so collection failures don't crash your scoring pipeline.

Use it for:

- Log model inputs and outputs in production Azure ML deployments to detect data drift or model degradation.
- Debug scoring pipeline issues by capturing intermediate data transformations before and after preprocessing.
- Correlate model predictions with their input data for post-hoc analysis and model validation.
- Collect training data for retraining by capturing live inference patterns in production.
- Audit model decisions by maintaining a timestamped record of what data was scored and what the model returned.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Collects model input and output data during Azure Machine Learning inference for monitoring and debugging, logging tabular data to configurable sinks at arbitrary points in a scoring pipeline.

Yes, if you are actively running models on Azure ML endpoints and need built-in data collection for monitoring. The low install friction and permissive license make adoption straightforward. However, the dormant maintenance status (841 days since last release) means you should verify compatibility with your current Azure ML SDK version and expect no active support or updates. Not suitable if you need ongoing development or are using non-Azure ML deployment platforms.

## Install

pip install azureml-ai-monitoring
uv add azureml-ai-monitoring
poetry add azureml-ai-monitoring

## Installing azureml-ai-monitoring

Before you install: Low install friction; depends only on pandas and requests. Maintenance is dormant—last release was 841 days ago, though the package reached v1.0.0 stable status. Suitable for existing Azure ML deployments but expect no active updates.

License in practice: MIT License (permissive) allows commercial and private use with minimal restrictions; you may use, modify, and distribute this package freely as long as you include the license notice.

Quickstart:

pip install azureml-ai-monitoring

from azureml.ai.monitoring import Collector
import pandas as pd

inputs_collector = Collector(name='model_inputs')
context = inputs_collector.collect(input_df)  # input_df is a pandas DataFrame

Requires Azure Machine Learning deployment environment with model data collection enabled in the deployment spec; local use is limited to capture only.

Verify before relying:

- Whether the package works with Python versions beyond 3.10 despite classifiers listing only 3.7–3.10.
- Current state of Azure ML integration—whether this SDK is actively used in recent Azure ML versions.
- Performance characteristics when logging high-volume data streams or large DataFrames.

## Package facts

- License: MIT License (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 102.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure ml model monitoring, collect model data during inference, azure machine learning data logging, model input output tracking, azure ml custom logging, model data collector sdk, azure ml observability, azure-ml, model-monitoring, data-collection

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