Table of contents

Watson Studio Local release versions on various platforms

Learn about the most recent releases of Watson Studio Local and the respective features implemented.

Platform Watson Studio Local version Release date Comments
POWER April 2019  
x86-64 March 2019  
HDP October 2018  
ICP for Data Science June 2018  
ICP for Data Science on POWER September 2018  
Linux on z October 2018 Supports RHEL and Ubuntu. GPU, R Shiny app, and Brunel visualization not supported.
IIAS November 2018  

Version features

x86 features
  • Installation configuration parameters secure the Watson Studio Local platform and users can remove the sshd and netcat services from applicable pods
  • Watson Studio Local platform is secured with updates to various components across all docker images
  • Cloudera Distribution for Hadoop version 5.16 and 6.0.1 support
POWER features
Silent installation support

Version 1.2.3 features

  • SAML support
  • NFS support
  • overlay2 support
  • Track usage scripts
  • Hive support in HDP Version 3.0.1
  • R jobs
  • Hadoop batch score and evaluate models
  • New Hadoop utility methods for models
  • Separate usage percentages in Admin Console
  • Admin Console pod search
  • Add Spark jars

Version 1.2.2 features

  • IBM Data Science Experience renamed to IBM Watson Studio
  • IBM Deployment Manager renamed to IBM Watson Machine Learning
  • Unicode (UTF-8) support for data sets
  • Access to HDFS data in HDP 3.0
  • Cluster troubleshooting and repair utility
  • Installation performance monitoring utility
  • OpenShift support (Beta)

Version 1.2.1 features

  • Jupyter terminal ( Launch terminal icon)
  • Any DSX Local user can install a library or package in the root conda environment and save it in a custom image
  • My Images page
  • Push custom images to a Hadoop cluster for remote jobs
  • Model groups
  • Roles for a project release: Deployment Admin, Admin, Developer, and Viewer
  • Project release Workers tab
  • Remote deployments (Beta)
  • Git merge conflict resolution
  • Separate Commit and Push
  • Restricted libraries and a Libraries page
  • Test a data source connection
  • Preview remote data sets for all supported JDBC data sources and custom JDBC data sources
  • Browse schema for most table types
  • SQL Query object type
  • Microsoft SQL Server support
  • Hyperledger Composer support
  • Refine data on a remote JDBC or HDFS data set
  • Data Refinery jobs
  • SSHD service panel in the Admin Console
  • Conda Management panel in the Admin Console
  • Search, filter, and sort projects. Bookmark web browser URLs for project searches
  • Support for Cognos Dashboards Embedded add-on
  • Support for Watson Explorer oneWEX add-on
  • Support for Spark Canvas add-on (Beta)

Version 1.2.0 features

  • Model Management and Deployment; requires license and deployment nodes
  • RHEL docker and RHEL packages now prerequisites instead of being included
  • Batch score or evaluate models created in RStudio
  • Batch score WML models with CSV files
  • Associate scripts with a model
  • Support for DSX Hadoop Integration clusters. Hadoop integration page
  • Decision Optimization Community Edition
  • Create data source to a custom JDBC
  • Apache Spark version 2.2.1 option
  • Support for unmanaged resources
  • All Active Environments page
  • Support for BitBucket Git repo
  • Commit history and tags for Git
  • Select files in project push
  • Import a clone of an external Git repository
  • Script editor
  • Test script as API option to generate a curl command
  • IBM Data Platform Manager is now called the Admin Console
  • Run configuration scripts from the Admin Console
  • Audit record of user login attempts
  • SPSS Modeler worker
  • Stop jobs
  • Data Refiner (Beta)

Version features

  • Jobs
  • Image management
  • Create data sources for IBM Big SQL, Cloudera Hive, Cloudera HDFS data, and a non-secure HDP cluster
  • Runtimes now called Environments. Environments can be associated with GPUs
  • Support for GPUs by NVIDIA in Azure, AWS and Softlayer. Support for GPU on Red Hat Enterprise Linux x86
  • Batch score or evaluate a model from the assets page, which creates a script that can be scheduled to run as a job
  • Model versioning
  • Support for model types: scikit-learn with pickle format, scikit-learn with joblib format, XGBoost, Keras TensorFlow, and WML
  • Publish models to the Model Management dashboard
  • Publish assets
  • Assets now saved in the cluster file system
  • Load balancer option in wdp.conf file install
  • Preview an R Shiny app
  • Automatic add-on install
  • Support for the SPSS Modeler Flows add-on with advanced visualizations

Version features

  • Library projects
  • Project runtimes that can reserve CPU cores and RAM memory. View all runtimes page
  • Create data sources for Hortonworks HDP: HDFS - HDP and Hive - HDP. Python utility functions to transfer files between the HDP cluster and the DSX Local cluster
  • Deploy models that can batch score remote data sets to an output CSV file
  • Support for model types: scikit-learn (for developing machine learning in a Python notebook) and XGBoost libraries
  • Import and score third-party vendor models using the Custom Batch or Custom Online option
  • Train models on a remote Spark using Livy REST APIs
  • Publish models to the Watson ML service
  • Project tree view ( The tree view icon)
  • Add more than one GitHub access token
  • Support for Flow add-on
  • Support for H20 Flow add-on