IBM has recently released Watson Studio and Watson Machine Learning 2.0.  This release brings together the code base for Watson Studio Cloud and Watson Studio Local (formerly Data Science Experience – DSX).

IBM Watson Studio is the data science and machine learning platform that helps companies 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.

In Watson Studio 2.0, IBM has added 43 data connectors such as Dropbox, Salesforce, Tableau and Looker for data exploration. An Asset Browser experience to navigate through Schemas, Tables and Objects has also been added. New tools for previewing and visualizing data have been added for refining data as well.

IBM has enhanced Watson Studio’s integrations with Hadoop Distributions (CDH and HDP) to be able to run analytics where your data lives and to leverage existing compute.

Watson Studio 2.0 also now includes built-in batch and evaluation job management for Python/R scripts, SPSS streams and Data Refinery Flows. A new collaborative interface, similar to Slack, has been added 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 IBM’s announcement made in February this year revealing their plans to make all 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.