Azure Machine Learning service is a cloud service that you use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides.
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’ve 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.
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. So 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.
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.
Azure Machine Learning service can autotrain a model and autotune it for you. For an example, see Train a regression model with automated machine learning.
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.
To get started using Azure Machine Learning service, see Next steps.
How is Azure Machine Learning service different from Machine Learning Studio?
Azure Machine Learning Studio is a collaborative, drag-and-drop visual workspace where you can build, test, and deploy machine learning solutions without needing to write code. It uses prebuilt and preconfigured machine learning algorithms and data-handling modules.
Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.
Use Machine Learning service if you work in a Python environment, you want more control over your machine learning algorithms, or you want to use open-source machine learning libraries.
This course will help you unravel that mystery as we use Azure Machine Learning to introduce you to machine learning and the technology behind it. You will see why companies are in such a rush to learn machine learning to grow their business and increase profits. You will learn how we can use Azure and Azure Machine Learning Studio to create machine learning predictive solutions, specifically, you will learn how to gather data, create machine-learning solutions that learn from that data, and evaluate their predictive power. Once we have our solution, we’ll deploy it via Azure. This will make our predictive solution available to users as a web service. And to ensure this service continues to provide great performance as data changes, we’ll go through the process of maintaining the solution. We will do much of our work with Azure Machine Learning Studio. Azure Machine Learning Studio lets us build much of our machine-learning solution by dragging and dropping modules onto a workspace, but it also lets us incorporate code written in R and Python into our solution. By the end of this course, you’ll know how to create, deploy, and maintain machine-learning solutions in Azure and make their predictive capabilities available to users worldwide
