https://github.com/fullstackwithlawrence/azureml-example
How to use the Microsoft Azure Machine Learning SDK for Python
https://github.com/fullstackwithlawrence/azureml-example
Last synced: 6 months ago
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How to use the Microsoft Azure Machine Learning SDK for Python
- Host: GitHub
- URL: https://github.com/fullstackwithlawrence/azureml-example
- Owner: FullStackWithLawrence
- License: agpl-3.0
- Created: 2025-07-02T15:32:29.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-07-02T16:40:57.000Z (6 months ago)
- Last Synced: 2025-07-02T16:46:01.922Z (6 months ago)
- Language: Python
- Size: 66.4 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Azure Machine Learning - Automated ML Example
[](https://azure.microsoft.com/en-us/products/machine-learning/)
[](https://www.python.org/)
[](https://github.com/FullStackWithLawrence/azureml-example/actions/workflows/test.yml)
[](https://www.gnu.org/licenses/agpl-3.0)
[](https://lawrencemcdaniel.com)
Demonstrate basic usage of Azure Machine Learning's [Automated ML](https://azure.microsoft.com/en-us/solutions/automated-machine-learning) service.
Note the following:
1. Model training is a computationally intensive task and it is not free. You'll need a paid [Azure Subscription](https://azure.microsoft.com/en-us/pricing/purchase-options/azure-account). Expect to spend in the neighborhood of $0.10/per hour ($USD) when training models.
2. This repo is currently based on Python3.9 even though this is several versions behind the latest stable version of Python. **DO NOT ARBITRARILY UPGRADE TO LATER VERSIONS OF PYTHON**. It will break. This repo is actively maintained. We monitor this. You have been warned.
3. This project leverages [Github Actions](https://github.com/features/actions) for automated unit tests, build, deploy, in addition to automating various administrative tasks including for example, automating updates to 3rd party package requirements. Most of these are visible from the [Actions](https://github.com/FullStackWithLawrence/azureml-example/actions) tab above.
## Usage
Works with Linux, Windows and macOS environments.
1. Verify project requirements: [Python 3.9](https://www.python.org/), [NPM](https://www.npmjs.com/) [Docker](https://www.docker.com/products/docker-desktop/), and [Docker Compose](https://docs.docker.com/compose/install/). Docker will need around 1 vCPU, 2Gib memory, and 30Gib of storage space.
2. Run `make` and add your credentials to the newly created `.env` file in the root of the repo.
3. Add your Azure `config.json` to the root of this project. See [Azure ML Configuration Guide](./docs/AZURE_ML_CONFIG.md) for detailed instructions on setting up an Azure Workspace and Subscription, and downloading your `config.json` file.
4. Add your Kaggle Api key to [.kaggle/kaggle.json](./.kaggle/kaggle.json), which you can generate from [https://www.kaggle.com/settings/account](https://www.kaggle.com/settings/account).
5. Initialize, build and run the application locally.
```console
git clone https://github.com/FullStackWithLawrence/azureml-example.git
make # scaffold a .env file in the root of the repo
#
# ****************************
# STOP HERE!
# ****************************
# Review your .env file located in the project root folder.
#
make init # Initialize Python virtual environment used for code auto-completion and linting
make test # Verify that your Python virtual environment was built correctly and that
# azureml.core finds your config.json file.
#
make docker-build # Build and configure all docker containers
make docker-run # Run docker container
```
## Support
Please report bugs to the [GitHub Issues Page](https://github.com/FullStackWithLawrence/azureml-example/issues) for this project.
## Developers
Please see:
- the [Developer Setup Guide](./docs/CONTRIBUTING.md)
- and these [commit comment guidelines](./docs/SEMANTIC_VERSIONING.md) 😬😬😬 for managing CI rules for automated semantic releases.
You can also contact [Lawrence McDaniel](https://lawrencemcdaniel.com/contact) directly.