Artificial Intelligence and Hybrid Cloud Take Center Stage at Microsoft Ignite – Forbes

Microsoft is placing its bets on Artificial intelligence (AI) and hybrid cloud. At Ignite 2017 in Orlando, Redmond emphasized on how AI has become the key ingredient of everything it’s developing. Azure Stack, the hybrid cloud offering is available to enterprise customers through select OEM partners.


Source: Microsoft

Satya Nadella, CEO, Microsoft

When Microsoft gets serious about an emerging technology, it starts with the developers. Microsoft is following the same path for making AI accessible. Firstly, it turned AI into a platform that becomes the foundation for both internal applications coming from Microsoft well as for external developers who can access it through APIs. It is then building a set of tools that make it easy for developers and data scientists to create AI-enabled applications.

Microsoft is committed to embedding AI into almost every new product and service. Microsoft Excel has got new formulae for performing predictive analytics in the cloud. PowerPoint has got a translator that can translate presentations in real time. Word is all set to have a new spell checker and grammar tool that goes beyond the basic correction. But, Office is just one of the products that will have powerful AI features. Dynamics CRM, SQL Server, Bing and many other services will exploit AI capabilities.

SQL Server 2017 is one of the first databases in the industry to get an embedded ML engine. Customers can mix and match existing SQL notations with predictive analytics. The ML engine supports R and Python languages along with modern libraries for training and visualization.

Machine Learning (ML) is at the heart of AI. To enable developers to embrace ML for building intelligent applications, Microsoft has unveiled an ML Workbench, which runs on both Windows and Mac. This tool is targeted at data scientists who are not users of Visual Studio – Microsoft’s flagship development environment. The ML Workbench deals with data cleansing and preparation, which forms the very first step in building ML models. It is integrated with popular open source data science toolkits such as Python Scikit Learn, Jupyter and Matplotlib. The best thing about the workbench is that it integrates with the cloud by seamlessly moving the heavy lifting to the GPU-powered VMs running in Azure.

For developers who are familiar with Visual Studio, there are extensions for popular ML frameworks such as CNTK, TensorFlow and MXNet.

Microsoft has also announced Azure Machine Learning Experimentation service for developers and data scientists to increase their rate of experimentation. Data scientists can use the service for training the models on their local machine, in Docker containers running locally or in the cloud, or on scale-out engines in Azure like Spark on HDInsight. The goal of this tool is to enable rapid iteration for evolving accurate ML models.

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