In a testament to how machine learning has evolved, all of these techniques have matured over the last decade. What's different now is the volume of data being generated, not just by humans and their activities but by all the machines and sensors plugged in and connected to the network, generating logs and observations. All of this data from all of these different sources can be combined and used to generate insights and make decisions faster and better than ever before.
And while Microsoft has been a big user and applier of machine learning for a while now, its Azure Machine Learning service puts the scale and power of one of the world's largest cloud platform operators into an easy-to-use package that takes just minutes and a credit card to get started with.
Off-the-shelf machine learning
Microsoft's Azure Machine Learning offering is a one-stop shop designed to get you started with cloud-based machine learning quickly and very easily. It starts in the Azure portal the same one where your Ops team spins up Azure virtual machines, configures storage options and provisions virtual networks to connect everything together where you can create a Machine Learning Studio (ML Studio) workspace and dedicated storage account.
This is the "partition" in the Azure service tenant in which all of the machine learning software resides. In the portal you can also monitor the consumption of the Azure Machine Learning service to keep track of expenses, receive alerts when a model is ready to be published, and deploy models as web services with the ML API Service so your models can integrate with your existing applications very easily.
Your data scientists will spend the majority of their time in the ML Studio experience. It's a friendly, drag-and-drop sort of experience, not a blank command line and an invitation to read a 900-page manual. You can execute every step in the data science workflow within the ML Studio, including accessing and preparing data; creating, testing and training models; importing your company's existing proprietary models securely into the private workspace; and more.
ML studio has support for the R statistical analysis language and includes the capability to work with raw R as well as over 300 of the most popular R packages, and in addition, Microsoft includes several ready-to-use algorithms that work alongside R. You can collaborate with colleagues anywhere with an Internet connection using the "share my workspace" feature, and finished models can be ready for consumption and use within minutes rather than having to set up and stage an entire BI or data environment.
The data that models within the ML studio can use can come from a variety of sources:
- Models can access data already in Azure.
- Models can query across Big Data in HDInsight.
- Models can pull datasets in right from the data scientists' desktops.
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