Watson Studio Desktop and Watson Machine Learning Server 1.1 Release

Adam Massachi
Jan 28 · 4 min read

Watson Studio Desktop and Watson Machine Learning Server offer the most flexible and powerful tools built for modern analysts and data scientists who understand that ease of use, time to insight, and automation are critical to successful data science and machine learning projects.

Together, Watson Studio Desktop and WML Server provide the open frameworks and expertly designed interfaces alongside best-by-test IBM technology that you need to be successful in solving the toughest problems with data.

Today, we’re proud to announce Watson Studio Desktop 1.1 and Watson Machine Learning Server 1.1.

Watson Studio Desktop 1.1

In December, we announced several exciting new feature additions to IBM Watson Studio Desktop Subscription. On January 31, 2020, several of those feature additions are coming to IBM Watson Studio Desktop 1.1.

First, Watson Studio Desktop users in the SPSS Modeler tool will now be able to view, edit and apply SQL directly to the data asset node for all supported databases. This is an extremely popular feature from SPSS Modeler that allows users to view and edit the SQL statement before query for maximum flexibility with database connections. Give it a try next time you use databases with SPSS Modeler in Watson Studio Desktop.

SQL Pushback to BigQuery

Second, we’ve added a new database connection. Users can now use Google BigQuery as an input and output data source in modeler flows, and they can push SQL back to BigQuery.

Third, we have provided a new Text Analytics example project so users can see the power of Text Analytics at work.

Watson Machine Learning Server 1.1

Before we first released Watson Machine Learning Server 1.0, we realized that many data science and analytics teams struggled with model management and deployment. We recognized that teams need a solution for managing their models and related artifacts like runtime details and data sets before deploying and then maintaining their models over time. Watson Machine Learning Server offers a mature client-server model for teams tackling these challenges and more.

Today, we are excited to announce the release of Watson Machine Learning Server 1.1! We learned a lot from our users and we’ve built 1.1 to solve the next set of challenges.

A Deployment Space in WML Server

A data science repository and framework for model operations

Watson Machine Learning Server 1.1 offers a mature data science asset repository that simplifies model operations such as storing models and runtime details, managing deployments and versions, and connecting to external applications via rich APIs. We feature the same APIs and UI elements as our on-premise and Public Cloud offerings, so users benefit from best-by-test APIs and workflows vetted in production deployments.

Virtual Machine Installation

In our research, we found that approximately 95% of customer use cases can benefit from VM installation. Virtual Machine Installation provides more flexible deployment options for clients.

A machine learning model deployment

Deploys analytical assets created in Watson Studio, or other open-source tools

Watson Studio Desktop features user interface integration with WML Server for managing models; other build environments like Jupyter notebooks or other Watson Studio deployments can manage deployments programmatically. This means that IBM and open source tools live side by side as first-class assets in your data science ecosystem.

Pushes compute to the data for lightweight scaling

Integration with Watson Studio Desktop means that WSD users can offload compute to WML Server and leverage remote execution engines for their SPSS Modeler Streams. WML Server includes Modeler Server.

Support Larger Environments with more than 32 vCPUs

Clients can now leverage all of the available cores in their target deployment environment.

Batch deployment using data assets promoted to deployment spaces

We’ve matured the batch deployment workflow for the most common use cases. This means that you can now specify entire data assets for scoring instead of just leveraging REST API calls to deployed models.

What you can do

Get expert help

If you are ready to speak with an expert to learn more about Watson Studio Desktop and WML Server, let us know here.

Sign up for a trial

Or check out the 30-day Watson Studio Desktop free trial today!

Check out the Cloud Offerings

Our cloud-first solutions offer a rich ecosystem of tools with the scalability and flexibility you demand from modern cloud offerings. Check out Watson Studio in the Cloud.

IBM Watson

AI Platform for the Enterprise

Adam Massachi

Written by

Watson Studio @ IBM

IBM Watson

AI Platform for the Enterprise

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