Data AMP 2017 just finished and some really interesting announcements came out specific to our company-wide push into infusing machine learning, cognitive and deep learning APIs into every part of our organization. Some of the announcements are ML enablers while others are direct enhancements.
Here is a summary with links to further information:
- SQL Server R Services in SQL Server 2017 is renamed to Machine Learning Services since both R and Python will be supported. More info
- Three new features for Cognitive Services are now Generally Available (GA): Face API, Content Moderator, Computer Vision API. More info
- Microsoft R Server 9.1 released: Real time scoring and performance enhancements, Microsoft ML libraries for Linux, Hadoop/Spark and Teradata. More info
- Azure Analysis Services is now Generally Available (GA). More info
- **Microsoft has incorporated the technology that sits behind the Cognitive Services inside U-SQL directly as functions. U-SQL is part of Azure Data Lake Analytics(ADLA)
- More Cortana Intelligence solution templates: Demand forecasting, Personalized offers, Quality assurance. More info
- A new database migration service will help you migrate existing on-premises SQL Server, Oracle, and MySQL databases to Azure SQL Database or SQL Server on Azure virtual machines. Sign up for limited preview
- A new Azure SQL Database offering, currently being called Azure SQL Managed Instance (final name to be determined):
- Migrate SQL Server to SQL as a Service with no changes
- Support SQL Agent, 3-part names, DBMail, CDC, Service Broker
- **Cross-database + cross-instance querying
- **Extensibility: CLR + R Services
- SQL profiler, additional DMVs support, Xevents
- Native back-up restore, log shipping, transaction replication
- More info
- Sign up for limited preview
- SQL Server vNext CTP 2.0 is now available and the product will be officially called SQL Server 2017:
Those I am most excited about I added ** next to.
This includes key innovations with our approach to AI and enhancing our deep learning compete against Google TensorFlow for example. Check out the following blog posting: https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/19/delivering-ai-with-data-the-next-generation-of-microsofts-data-platform/ :
- The first is the close integration of AI functions into databases, data lakes, and the cloud to simplify the deployment of intelligent applications.
- The second is the use of AI within our services to enhance performance and data security.
- The third is flexibility—the flexibility for developers to compose multiple cloud services into various design patterns for AI, and the flexibility to leverage Windows, Linux, Python, R, Spark, Hadoop, and other open source tools in building such systems.