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Anaconda brings Python to Microsoft, machine learning in analytics catches fire and more in today’s data science news.

Anaconda, Microsoft team up to deliver Python-Powered Machine Learning on MS products including Azure

Python data science leader Anaconda has entered into a partnership with Microsoft to deliver “Anaconda for Microsoft”, a distribution that will be available on Windows, MacOS and Linux. With a range of support, Anaconda will be integrated into Azure Machine Learning, Visual Studio and SQL Server. Python codes will now be able to run inside SQL Server cutting down on the requirement to export data, thus improving Python data science performance. Under the partnership, Anaconda users who use R programming language with Anaconda’s R Essentials package can now also avail Microsoft R Open packages. “With Anaconda distributing Microsoft R and Microsoft including Anaconda distribution in Microsoft SQL Server, Microsoft Azure Machine Learning Services and Microsoft Visual Studio, our data platform and cloud customers can do high performance analytics and machine learning with some of the best open source and proprietary frameworks available,” said Joseph Sirosh, Corporate Vice President, Data Group, Microsoft Corp. “By combining both R and Python in SQL Server, we are facilitating the expansion of data science across the enterprise.


IBM unveils Integrated Analytics System for high-performance data science

IBM has introduced a unified data system called the Integrated Analytics System that will support advanced analytics across public, private, or hybrid cloud platforms, including IBM BigSQL and IBM Db2 Warehouse On Cloud Hadoop. Built with IBM common SQL engine and embedded with IBM Data Science Experience and Apache Spark, the new data system simplifies machine learning processing as the data does not have to be moved now for analytics processing where the analytic will take its own time to respond. This gives the users faster and easier access to data science.

Splunk boasts of machine learning capabilities across its platform

Splunk has expanded its machine learning capabilities further, after deciding to start building in machine learning last year. Key new features include addition of a data cleaning tool, integration of necessary machine learning APIs, and added management support to import user permissions directly into machine learning platforms. Splunk ITSI 3.0 (the new version) can even identify and prioritize issues based on the criticality of the operation. The new advancements are in line with Splunk’s future plan to intelligently automate the task of data monitoring (alerting humans only when it is absolutely required) as customers will soon need tools to cope up with the increasing amount of data and alerts.

MemSQL 6: Now developers can run Machine Learning algorithms in SQL environment

Popular in-memory database MemSQL has gifted developers machine learning features in its newest version released yesterday. This further brings operational applications closer to data science. “As machine learning use expands within companies, a smarter database is needed to maximize learning,’ MemSQL CEO Nikita Shamgunov said in the announcement, “MemSQL 6 now has capabilities such as extensibility to enable more ML functions, so customers can meet the needs of their most ambitious challenges.”


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