For Microsoft, the last year or so has been all about infusing its various products with intelligence. Those smarts may come in the form of advanced analytics or deeplearning, but the main point here is that the company believes these techniques can solve major pain points for its customers, whether they are PowerPoint users or SQL Server admins. Today, at its Data Amp event, the company is launching a number of updates that make it easier for developers who use Microsofts tools to bring intelligence to their own apps.
At todays event, Microsoft islaunching updates to the preview of SQL Server 2017 (technically, this is the Community Technology Preview 2.0), which will likely become generally availablelater this year (no surprise, givenits name), as well as its Cognitive Services. SQL Server 2017, its worth noting, is also the first version to run on Linux and in Docker containers.
As Microsofts Rohan Kumar, the companys general manager for database systems, told me, the basics of database management have essentially been commoditized now.What users are looking for now is better ways to get value out of the data they are storing. What Microsofts partners were telling it was that it was still too hard to build intelligence into applications and that they wanted more of that intelligence to be directly available in the database.
So with todays update, Microsoft announced that it is building support for Python right into SQL Server 2017. That may sound like a minor thing at first, but the vast majority of machine learning frameworks rely on Python.Making it easier to access Python scripts right from the database means its also easier to use machine learning techniques on this data instead of having to first move it out of the database, let it go stale and thentry to build machine learning models on it.
Indeed, its this idea of bringing data and AI closer together thats driving many of these decisions. As Joseph Sirosh, Microsofts corporate vice president for its Data Group, noted during a small press event in Seattle earlier this month, intelligence should reside right next to the data. It should reside in databases. It should reside in applications that generate data. So bringing intelligence to our data platforms is an incredibly important part of our strategy.
The advantage ofbringing the intelligence closer to the data is pretty obvious in that you can run applications much faster when theres less latency involved and thats especially true given that the data sets used for machine learning tend to be extremely large.
Besides the added support for Python, Microsoft also is improving its existing support for R a language thats extremely popular with data scientists in SQL Server 2017. And as part of this, the company is adding pre-trained neural networks for sentiment analysis and image featurization right into R Server.
To make SQL Server 2017 more useful for more applications, the company also is addingnew features for working with graph datato its database. This, the company says, will make it easier for developers to represent the hierarchies and relationships that already typically exist in these relational databases without having to go to a specialized third-party graph database.
MicrosoftCognitive Services, which makes pre-packaged machine learning models available to developers, also is getting an update today. Thats a minor update, though, and mostly focuses on the launch of the Face API, Computer Vision API and Content Moderator into general availability.
Other updates announced today include the general availability of the Azure Analysis Services and new templates for Cortana Intelligence Services thatoffer pre-built solutions for quality assurance and personalized offers.
All of these new features, though, are mostly a manifestation of the companys overall vision to bringdata and the intelligence to analyze it closer together.