IBM has updated Watson Studio, the data science and machine learning platform that helps you build, train and deploy AI models. With the ability to tap into on-premise data repositories and deploy models to local data centers or in the cloud, the continued investments in Watson Studio are part of IBM's effort to cater to the enterprise's hybrid and multi-cloud needs.
Watson Studio 2.0 includes a range of new features, starting with data preparation and exploration. For data exploration, IBM is adding 43 data connectors like Dropbox, Salesforce, Tableau and Looker. It's also adding an Asset Browser experience to navigate through Schemas, Tables and Objects. For refining data, there are new tools for previewing and visualizing data.
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For running analytics where your data lives and to leverage existing compute, IBM has enhanced Watson Studio's integrations with Hadoop Distributions (CDH and HDP).
Watson Studio 2.0 also now includes built-in batch and evaluation job management for Python/R scripts, SPSS streams and Data Refinery Flows. There's a new collaborative interface, similar to Slack, to the Jupyter Notebook integration. Version 2.0 also lets scientists import open source packages or libraries.
For accessing and editing different versions of assets, IBM has added support for major GIT frameworks including Github, Github enterprise, BitBucket and BitBucket server.
The rollout of Watson Studio 2.0 follows an announcement earlier this year that IBM would make all of its Watson cognitive and AI technology portable to multiple clouds. Watson Studio, along with the rest of Watson's applications, developer tools and models, are now a part of IBM Cloud Private (ICP) for Data.
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