Azure Machine Learning service provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. Azure Machine Learning service fully supports open-source technologies. You can use tens of thousands of open-source Python packages with machine learning components. Examples are TensorFlow and scikit-learn. Support for rich tools makes it easy to interactively explore data, transform it, and then develop and test models. Examples are Jupyter notebooks or the Azure Machine Learning for Visual Studio Code extension. Azure Machine Learning service also includes features that automate model generation and tuning to help you create models with ease, efficiency, and accuracy.

With Azure Machine Learning service, you can start training on your local machine and then scale out to the cloud. With many available compute targets, like Azure Machine Learning Compute and Azure Databricks, and with advanced hyperparameter tuning services, you can build better models faster by using the power of the cloud.

By using the Azure Machine Learning SDK for Python, along with open-source Python packages, you can build and train highly accurate machine learning and deep-learning models yourself in an Azure Machine Learning service Workspace. You can choose from many machine learning components available in open-source Python packages, such as the following examples:

• Scikit-learn
• Tensorflow
• PyTorch
• CNTK
• MXNet

After you have a model, you use it to create a container, such as Docker, that can be deployed locally for testing. After testing is done, you can deploy the model as a production web service in either Azure Container Instances or Azure Kubernetes Service. For more information, see the article on how to deploy and where. Then, you can manage your deployed models by using the Azure Machine Learning SDK for Python or the Azure portal. You can evaluate model metrics, retrain, and redeploy new versions of the model, all while tracking the model’s experiments.