Technology Convergence Culminates in IBM Watson Machine Learning V2.0

Greg Filla
May 2 · 4 min read

By Armand Ruiz, Stevan Slusher, Adam Massachi, Greg Filla, Julianna R. Delua, Yin Chen and Vishnu Alavur Kannan

At the intersection of data, mathematics, and business you’ll find the most exciting area of technology today. With a background in business, I began to consider the impact data science could have in real-world applications very early on in my career. Over the years since the open source community and amazing evolution of tools and techniques developed around data science and machine learning (ML) have pushed these technologies to maturity. They now positively impact our businesses, our lives, and the world.

Today, I’m excited to announce another step in that evolution of data science with IBM® Watson® Machine Learning (WML) V2.0. WML is a convergence of divergent technologies that enables data scientists and citizen data scientists to more easily and efficiently deploy self-learning models into production at scale.

Embed ML in your applications with WML

Now, let’s take a look at the foundational benefits offered by WML, the enhancements afforded by V2.0 and the new tools that data scientists, DevOps and application developers will be using on their continued journey to AI.

Deploy and manage models at scale

WML also allows you to perform critical tasks beyond the deploy phase. Once deployed, your models need to be well-managed. For that result, we included versioning controls and the ability to build automation pipelines around models, preventing them from going stale or losing the quality of accuracy in performance over time.

Deployment spaces overview in Watson Machine Learning Local 2.0

Enable intelligent model operations

Also, WML will retrain and redeploy models as needed and based on how they’re performing in the system or application. This prevents lengthy stretches where your prediction accuracy is degrading; users are notified of any such degradation more quickly. As a result, the models will be retrained more reliably due to automation.

SPSS Modeler deployment details in Watson Studio Local 2.0

Accelerate compute-intensive workloads

Once a job is in the WML Accelerator environment, WML Accelerator will schedule it and any other jobs currently in the pipeline in the most efficient fashion, allowing all batch training jobs in the current pipeline to be completed quicker than ever before.

This process can be managed and monitored via an easy-to-use built-in user interface (UI), that extends to most areas of WML. In addition, with dynamic resource allocation, we’re making sure that jobs with higher priorities are having the most resources allocated to them.

Tensorflow training executed with Watson Machine learning Accelerator

Bring it all together seamlessly across multiple clouds

Want to train a model on your on-premises environment and deploy to a hosted cloud? You can easily do so with WML 2.0 thanks to its hybrid nature.

WML 2.0 utilizes a common API between cloud and local, meaning that not only can you work seamlessly across environments, you’ll also enjoy programmatical interaction whether using the IBM Watson Machine Learning API, Command Line Interface, or Python Client.

Innovations that simplify the use of models for all

Want to know more? View the supporting blogs of my colleagues below. Vishnu Kannan covers the capabilities of Watson Studio V2.0 and how the technology works cohesively with WML. Julianna Delua covers the overarching benefits of the combined technologies and how they benefit modern enterprise. Or, you can read the Enterprise Strategy Group (ESG) technical validation for even more detail.

IBM Watson

AI Platform for the Enterprise

Greg Filla

Written by

Product Manager, IBM Watson Machine Learning - helping organizations operationalize their data science.

IBM Watson

AI Platform for the Enterprise

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