Azure Machine Learning is a cloud service that you use to train, deploy, automate and manage learning models, all at the broad scale that the cloud providers.
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What is machine learning?
Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes and trends. By using machine learning, computers learn without being explicitly programmed. Forecasts or predictions from machine learning can make apps and devices smarter. For example, when you shop online, machine learning helps recommend other products you might want based on what you have bought. Or when your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. And when your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.
What is Azure Machine Learning service?
Azure Machine Learning service provides a cloud-based environment you can use to prep data, train, test, deploy manage and track machine learning models.
Azure Machine Learning service fully supports open-source technologies. So, you can use tens of thousands of open-source Python packages with machine learning components. Examples are PyTorch, TensorFlow and scikit-learn. Support for rich tools makes it easy to interactively explore and prepare data 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 the model generation and tuning to help you create models with ease, efficiency and accuracy.
By using 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.
When you have the right model, you can easily deploy it in a container such as Docker. So, it’s simple to deploy to Azure Container Instances or Azure Kubernetes Service. Or you can use the container in your own deployments, either on-premises or in the cloud. For more information, see the article on how to deploy and where.
You can manage the deployed models and track multiple runs as you experiment to find the best solution. After it’s deployed, your model can return predictions in real-time or asynchronously on large quantities of data. And with advanced machine learning pipelines, you can collaborate on all the steps of data preparation, model training and evaluation and deployment.
What can I do with Azure Machine Learning service?
Using the main Python SDK and the Data Prep SDK for Azure Machine Learning as well as 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 open0source Python packages, such as the following examples:
Azure Machine Learning service can also autotrain a model and autotune it for you. 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. 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 experiments.
Deploy models with the Azure Machine Learning service
The Azure Machine Learning SDK provides several ways you can deploy your trained model. You can deploy models to the following compute targets: