Watson Studio Desktop and Watson Machine Learning Server 2.0 Beta is now open for registration
By: Yin Chen
Tags: Machine Learning, Artificial Intelligence, Data Science, Automation, Business Intelligence

Watson Studio Desktop and Watson Machine Learning Server enable business analysts and data scientists to get insights and create values from data easier and faster with a wide range of tools. You can build models automatically with the new AutoAI capabilities, drag-and-drop capabilities of SPSS Modeler, or write code using open source tools like Jupyter Notebooks. You can also offload model training workload, deploy and manage models through Watson Machine Learning Server easily with graphical UI.

With the 2.0 Beta, Watson Studio Desktop and Watson Machine Learning Server will be more flexible and powerful than ever. Let’s take a look at the new features.

For clarification, all mentions of SPSS Modeler refer to the version within Watson Studio Desktop, not the classic standalone SPSS Modeler.

Build machine learning models with AutoAI

AutoAI Experiment UI

The AutoAI graphical tool in Watson Studio automatically analyzes your structured data and generates candidate model pipelines customized for your predictive modeling problem. These model pipelines are created iteratively as AutoAI analyzes your data set and discovers data transformations, algorithms, and parameter settings that work best for your problem settings.

AutoAI is NOT a vendor lock-in black box solution. You can save model pipelines to your project as an automatically generated Python notebook where every step is clearly written and reproducible in other open-source environments.

Please take a look at these AutoAI demos to learn more about its applications in different industry use cases.

New enhancements in SPSS Modeler

New CPLEX Optimization Node

SPSS Modeler adds several new nodes including the new CPLEX Optimization node. It can build and solve complex optimization models to identify the best possible actions using mathematical and constraint programming. You can learn more about how CPLEX optimization delivers cost savings on this page.

Besides new nodes, the Data Asset Export node has been redesigned. You can use the node to write to remote data sources using connections, write to a data file on your local computer, or write data to your project.

Also, you can now deploy Text Analytics models to a Watson Machine Learning Server as you can with other model types. And there are many new enhancements in SPSS Modeler not mentioned here due to limited space.

New visualization charts for Data Refinery and SPSS Modeler

Evaluation Chart

Getting insights from data is much easier now with new visualization charts available to build interactive and informative reports.

The list of new charts includes but is not limited to:

Bubble charts display each category in the groups as a bubble.

Circle packing charts display hierarchical data as a set of nested areas.

Evaluation charts are combination charts that measure the quality of a binary classifier.

Math curve charts display a group of curves based on equations that you enter.

Multi-charts display up to four combinations of Bar, Line, Pie, and Scatter plot charts.

Radar charts integrate three or more quantitative variables that are represented on axes (radii) into a single radial figure.

Sunburst charts display different depths of hierarchical groups.

Tree charts represent the hierarchy in a tree-like structure.

Theme river charts use a specialized flow graph that shows changes over time.

Time plot charts illustrate data points at successive intervals of time.

New operations and enhancements for Data Refinery

Data Refinery Join operation

With the new Join operation, users can join two data sets in a variety of ways. You can perform a full join, inner join, left join, right join, semi-join, or anti join. You can also select the columns you want to see in the result set, and if there are same-named columns between the two data sets, you can specify unique suffixes to differentiate them.

Also, you can now use the new Union operation to combine the rows from two data sets that share the same schema.

Besides new operators, there are many enhancements to existing operations. For example, you can now perform aggregate calculations on multiple columns. And when you open a file with date and timestamp data, they are automatically detected and converted to inferred data types.