What’s New with Watson Machine Learning?

Greg Filla
Feb 12 · 3 min read

Update: to learn about the General Availability of AutoAI read here!

For many modern businesses, leading the market requires machine learning.

Here at IBM we are dedicated to solving the toughest problems and the rapidly evolving demands of enterprise AI. Strategic goals for the most competitive organizations include infusing AI across the business. For example, injecting predictive power into micro-service-based applications is a powerful pattern that has remained at the top of the agenda for many real world wins with AI.

At IBM Watson Machine Learning, we see three broad themes in this ecosystem:

  • Acceleration of ML/DL workloads: As Armand Ruiz mentioned in his blog, with the announcement of Watson Machine Learning Accelerator add-on (formerly, Power AI Enterprise), we expanded our Watson Studio and Watson Machine Learning family! The new Watson Machine Learning Accelerator add-on to Watson Studio and Watson Machine Learning helps you drive virtually unlimited scale while optimizing the computing resource power of your private clouds. We have a few approaches to the concept of “acceleration.” First, we mean “accelerate” as optimizing the infrastructure in place for running compute intensive workloads like training deep neural networks. Second, workloads are “accelerated” through a productivity boost, realized from the improved collaborative functionality and job management core to these services.
  • Scaling and achieving multicloud competency of ML/DL workloads. With 94% of our enterprise clients using multiple clouds, driving value from your AI initiative requires that you know how to tackle movement, connectivity and consistency for your apps, data, and algorithms with end-to-end security. Designed to simplify your journey to the cloud and powering your AI initiative, IBM built IBM Cloud Private for Data and Watson Studio and Watson Machine Learning are the core capabilities behind this popular offering.
  • Balancing auto- and manual processes of the data science lifecycle- Simplify AI for all. After years of research and experimentation, autoAI/ML capabilities are on the cusp of reaching the level of progression that could further simplify the data science process end-to-end. This is one of the key topics of interest of IBM clients and prospects. As such, we are excited to announce our autoAI/ML preview — join the preview here!
Evaluating an example pipeline created with AutoAI in Waston Studio

We are continuously learning about use cases and prioritizing our development efforts based on the business opportunities presented by working closely with our clients. If you are interested in automating your machine learning pipeline development and want to help guide the future of this capability from IBM, join our waitlist and subscribe to this blog publication to be notified on the latest news.

We are also providing a deep dive of these topics at February 12–15, IBM Think 2019 in San Francisco. You can join:

IBM Watson Machine Learning Deep-Dive — February 14, Thursday, 11:30 AM — 12:10 PM | Session ID: 7713A | Moscone South, Level 3 | Room 303

Demo Pedestal — Analyze: IBM Watson Studio & Machine Learning — Moscone South, Data & AI Campus # 365–12

To learn more about how Watson Studio and Watson Machine Learning help your enterprise AI initiatives, please download ESG Technical Validation Paper on Watson Studio and Watson Machine Learning.

You can see Watson Machine Learning in action on February 26 Webinar about transforming customer experience. In addition, you can also register here for our March 26 Webinar to get the latest on Watson Machine Learning and Watson OpenScale . You can also check out our refreshed Watson Machine Learning website or try out Watson Machine Learning product tour!

IBM Watson

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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