Let me now tell you about three patterns of Innovations – the Intelligence DB, Intelligent Lake and Deep Intelligence.
They are 3 patterns for developing and deploying intelligence @ scale.
The basic premise of IntelligenceDB pattern is that you “push intelligence to where your data lives”. When you do this with an industrial strength database engine like SQL Server, you can get throughput, parallelism, security, reliability, compliance certifications and manageability, all in one. It’s a big win for developers – you don’t have to build it separately.
Furthermore, just like data in databases can be shared across multiple applications, you can now share the predictive models. Models and intelligence beome “yet another type of data”, managed by the DBMS.
1. Bring intelligence to where your data lives
2. On the most trusted and performant plat
3. With any language, any platform, anywhere
A few weeks ago, we announced SQL Server 2017 CTP 2.0, the first commercial database with AI built-in.
By adding Python support in addition to R and adding real-time scoring capabilities, now you can run machine learning models directly in SQL Server to eliminate the need to move data, increase efficiency and help uncover new insights.
You can easily incorporate AI models into SQL queries, allowing you to infuse your applications with intelligence with minimal extra coding.
It also supports graph data, enabling efficient analysis of complex relationships.
And the database server uses machine learning internally to adaptively process queries for best possible performance.
It Supports Linux distributions including RedHat Enterprise Linux (RHEL), Ubuntu, and SUSE Enterprise Linux (SLES)
You can run SQL Server in Windows and Linux containers on Docker
It’s an amazing harness for AI applications.
Native integration of Python in SQL Server – best of the both worlds
Deep learning database apps with GPU and open source Python packages
The only commercial database server for AI
In Azure we can integrate the power of Cognitive Intelligence and Data Lakes for processing massive data.
I call this Big Cognition.