| 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 |
 |
2d451e883b
