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

# azureml-train-automl-client — Used for automatically finding the best machine learning model and its parameters.

License: unclear · Maintenance: active · Downloads: 189.4K/mo

## What it is and what it does

azureml-train-automl-client is a Python client for Azure Machine Learning's automated machine learning service. It provides the interface to submit training and test data and automatically explore model types, algorithms, and hyperparameters to find the best-performing model for your problem. The package integrates with azureml-automl-core, azureml-core, azureml-dataset-runtime, azureml-telemetry, and azureml-train-core.

Typically used within Azure ML workflows, this client abstracts away the complexity of manual model selection and tuning. It supports Python 3.8, 3.9, 3.10, and 3.11 on macOS, Windows, and Linux. The package is production-stable and actively maintained, making it suitable for teams already invested in the Azure ML ecosystem who want to reduce the time spent on model experimentation.

Use it for:

- Submit a classification or regression dataset to Azure AutoML and retrieve the best model without manually trying multiple algorithms.
- Automate hyperparameter tuning for a specific model type when you have a large search space.
- Integrate automated model selection into an Azure ML pipeline or batch training job.
- Benchmark multiple model families on your dataset to identify which algorithm class performs best.
- Reduce data science iteration time by letting AutoML explore model combinations in parallel.

## Worth the install?

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

Automatically selects and tunes machine learning models given training and test data, handling model search and hyperparameter optimization as part of Azure ML's AutoML workflow.

Yes, if you are already using Azure ML and want to automate model selection and tuning within that ecosystem. The package is production-stable, actively maintained, and has low install friction. No, if you need a standalone AutoML solution that does not require Azure services or if your project uses a different cloud provider or on-premises infrastructure.

## Install

pip install azureml-train-automl-client
uv add azureml-train-automl-client
poetry add azureml-train-automl-client

## Installing azureml-train-automl-client

Before you install: Low install friction with a pure Python wheel. Active maintenance status as of 170 days since last release. Depends on azureml-automl-core, azureml-core, azureml-dataset-runtime, azureml-telemetry, and azureml-train-core.

License in practice: Licensed under a proprietary Microsoft license (https://aka.ms/azureml-sdk-license). License terms are not SPDX-identified, so review the linked license before use in proprietary or open-source projects.

Quickstart:

pip install azureml-train-automl-client
from azureml.train.automl import AutoMLConfig

Requires Azure ML workspace setup and authentication credentials to submit AutoML jobs.

Verify before relying:

- Whether Azure ML workspace and authentication setup is required before this package can be used.
- What compute or resource constraints apply when running AutoML jobs through this client.
- Whether the package includes local model training or only submits jobs to Azure services.

## Package facts

- License: https://aka.ms/azureml-sdk-license (unclear)
- Python support: capped_below_current
- Install friction: low
- Maintenance: active
- Downloads: 189.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

automated machine learning model selection, automl hyperparameter tuning, automatic model optimization, azure ml automl client, ml model search and tuning, automl pipeline azure, automl, azure-ml, hyperparameter-tuning

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