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sample |
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Top-level directory for official Azure Machine Learning Python SDK v2 tutorials. |
- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
- Install the SDK v2
pip install azure-ai-ml
git clone https://github.com/Azure/azureml-examples
cd azureml-examples/tutorials
Test Status is for branch - main
| Title | Notebook | Description | Status |
|---|---|---|---|
| azureml-getting-started | azureml-getting-started-studio | A quickstart tutorial to train and deploy an image classification model on Azure Machine Learning studio |  |
| azureml-in-a-day | azureml-in-a-day | Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage. |  |
| e2e-distributed-pytorch-image | e2e-object-classification-distributed-pytorch | Prepare data, test and run a multi-node multi-gpu pytorch job. Use mlflow to analyze your metrics |  |
| e2e-ds-experience | e2e-ml-workflow | Create production ML pipelines with Python SDK v2 in a Jupyter notebook |  |
| get-started-notebooks | cloud-workstation | Notebook cells that accompany the Develop on cloud tutorial. |  |
| get-started-notebooks | deploy-model | Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2. |  |
| get-started-notebooks | explore-data | Upload data to cloud storage, create a data asset, create new versions for data assets, use the data for interactive development. |  |
| get-started-notebooks | pipeline | Create production ML pipelines with Python SDK v2 in a Jupyter notebook |  |
| get-started-notebooks | quickstart | no description |  |
| get-started-notebooks | train-model | no description |  |
We welcome contributions and suggestions! Please see the contributing guidelines for details.
This project has adopted the Microsoft Open Source Code of Conduct. Please see the code of conduct for details.