Data Science with Microsoft Azure

Using Azure Machine Learning Services

Azure is Microsoft’s well-known cloud platform, competing with the Google Cloud and Amazon Web Services. Microsoft Azure gives us the freedom to build, manage, and deploy applications on a massive global network using our favourite tools and frameworks. Azure provides over 100 services that enable us to do everything from running our existing applications on virtual machines to exploring new software paradigms such as intelligent bots and mixed reality. It also provides storage solutions that dynamically grow to accommodate massive amounts of data. Azure services enable solutions that are simply not feasible without the power of the cloud. Here we are going to mainly focus on the Azure Machine Learning services.

Azure Machine Learning services is a relatively new addition to Microsoft Azure, released publicly in December 2018. Azure Machine Learning services contains many advanced capabilities designed to simplify and accelerate the process of building, training, and deploying machine learning models. Support for popular open-source frameworks such as PyTorch, TensorFlow, and scikit-learn allows data scientists to use the tools of their choice.

Azure ML services provide multiple services and we will look at a few of those here. We will talk about Notebook VMs, which provide a Jupyter notebook interface for experimenting, Visual Interface, which provides a simple drag and drop interface suitable for rapid prototyping and at the end we will look at its Visual Studio Code extension and Python SDK to create experiments and pipelines. We will explain, step by step, how the ML services are used in an actual project.

We start by creating a workspace, which is a place in Azure for us to store our work. We can create a workspace in the Azure portal or via Python code. It’s easy to create a workspace through the Azure portal, just open All services, select AI+Machine Learning and then Machine Learning service workspaces. To create it, we need to provide a name for our workspace, choose the right subscription account, choose a resource group or create a new one and choose a location for our workspace. Choosing a location is an important factor, as everything created in this workspace will be in the…

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