---
id: azureml-telemetry
version: "1.62.0"
license: https://aka.ms/azureml-sdk-license
license_treatment: unclear
maintenance: active
---

# azureml-telemetry — Used to collect telemetry data like Log messages,     metrics, events, and activity messages

License: unclear · Maintenance: active · Downloads: 348.8K/mo

## What it is and what it does

azureml-telemetry is Microsoft's Python package for collecting and sending telemetry data to Application Insights, typically used within Azure Machine Learning workflows. It provides a logging interface to capture messages, metrics, events, and activity traces from your code, supporting both free-text and structured logging modes. The package wraps the underlying applicationinsights library and is designed to integrate with Azure's monitoring and diagnostics infrastructure.

The package is part of the Azure ML ecosystem and is most useful when you're already working within Azure ML experiments or pipelines and need to send observability data to Application Insights. It has low install friction (one dependency) and active maintenance, supporting current Python versions (3.7+). The license is unclear because it points to a Microsoft URL rather than a standard open-source identifier, so you should verify the terms if you're using it in a proprietary or copyleft context.

Use it for:

- Log custom metrics and events from Azure ML training runs to Application Insights for post-experiment analysis.
- Capture structured diagnostic data during model training to monitor code behavior and performance.
- Send activity traces from ML pipelines to centralized Azure monitoring for troubleshooting and auditing.
- Instrument Python scripts running in Azure ML compute to collect free-text or metric-based telemetry.
- Track experiment metadata and custom events alongside Azure ML's built-in logging for richer observability.

## Worth the install?

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

Collects telemetry data from Python code by logging messages, metrics, events, and activity to Application Insights for monitoring and diagnostics.

Yes, if you are working within Azure ML and need to send telemetry to Application Insights. The package has low install friction, active maintenance, and no known vulnerabilities. The main caveat is the unclear license—review the Microsoft URL before use in proprietary projects. If you are not already in the Azure ecosystem, this package is unlikely to be useful.

## Install

pip install azureml-telemetry
uv add azureml-telemetry
poetry add azureml-telemetry

## Installing azureml-telemetry

Before you install: Low install friction with a single runtime dependency (applicationinsights). Active maintenance status with a recent release (170 days ago), supporting current Python versions.

License in practice: License treatment is unclear—the package points to a Microsoft URL (https://aka.ms/azureml-sdk-license) rather than a standard SPDX identifier, so you should review the actual license terms before use in proprietary or copyleft projects.

Quickstart:

pip install azureml-telemetry

from azureml_telemetry import get_telemetry_log
log = get_telemetry_log()
log.log_event('event_name', {'key': 'value'})

Requires Python 3.7 or later (supports up to Python <4). Application Insights backend connectivity needed for telemetry transmission.

Verify before relying:

- Whether the package works standalone or requires broader Azure ML SDK integration to function.
- Specific telemetry data types and structured logging formats supported beyond the description's examples.
- Whether free-text vs. structured logging modes have different performance or storage implications.

## Package facts

- License: https://aka.ms/azureml-sdk-license (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 348.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure machine learning telemetry, application insights logging python, azure ml monitoring, telemetry collection framework, application insights integration, structured logging azure, ml experiment tracking, azure-integration, observability

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