Predict What's Next: Getting Started on Azure Machine Learning – Part 1

Earlier this summer at WPC, we announced the preview of Microsoft Azure Machine Learning, a fully-managed cloud service for building predictive analytics solutions. With this service, you can overcome the challenges most businesses have in deploying and using machine learning. How? By delivering a comprehensive machine learning service that has all the benefits of the cloud. In mere hours, with Azure ML, customers and partners can build data-driven applications to predict, forecast and change future outcomes – a process that previously took weeks and months.

But once you get your hands on Azure ML, what do you do with it? Some examples we already see happening include:

  • Consumer oriented firms with targeted marketing, churn analysis and online advertising
  • Manufacturing companies enabling failure and anomaly forecasting for predictive maintenance
  • Financial services companies providing credit scoring, bankruptcy prediction and fraud detection
  • Retailers doing demand forecasting, inventory planning, promotions and markdown management
  • Healthcare firms and hospitals supporting patient outcome prediction and preventive care.

So how can machine learning impact your organization? Walk through these tutorials and start exploring the possibilities. The video tutorials and learning resources below will help you to quickly get up and running on Azure ML.

  1. Create an Azure Account
    Before you begin, you must create an Azure account. Create a free trial here.

  2. Overview of Azure ML: Watch an overview of the Azure Machine Learning service: a browser-based workbench for the data science workflow, which includes authoring, evaluating, and publishing predictive models.


     
  3. Getting started with Azure ML Studio: Walk through a visual tour of the Azure Machine Learning studio workspaces and collaboration features.


     
  4. Introduction to Azure ML API Service: Learn about the Azure Machine Learning API service capabilities.


     
  5. Provisioning Azure ML Workspaces: Walk through steps needed to provision a Machine Learning workspace from the Azure Portal.

     

Look for more video tutorials later this week, when we’ll cover getting and saving data in Azure ML, pre-processing that data, how we handle R in Azure ML Studio, and deploying predictive models with Azure ML.

In the meantime, there are a ton of resources you can use to continue your learning: