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Keeping IBM Machine Learning on z/OS Next to the Data Enhances Analytics

IBM Machine Learning for z/OS
Illustration by Richard Mia

Machine learning isn’t some sort of sci-fi-y attempt to make mechanical overlords. Rather, it’s a method by which users can train computers to recognize and even predict patterns in data and, based on machine-learning models, help them make better decisions based on historical, recent and up-to-the-minute information—no matter the format.

Machine learning represents an acute shift from traditional data-harvesting models, with dynamic data modeling and cognitive processing taking the place of the data warehousing, a comparatively clunky way to derive value from data. This is especially true now that data has become a continuous stream of vital information that, if modeled properly, can lead to previously undiscovered insight. And this is what makes the IBM z Systems* environment a perfect machine learning platform.

“You’re taking advantage of the proximity of the data and allowing for faster processing based on IBM mainframe technology,” says Nick Sardino, program director, IBM z Systems Offering Management. “And from a business-value perspective, there are hidden patterns of understanding you may not have visibility to when using other methods. That’s the really the big story when it comes to machine learning on z Systems.”

Rich Structure

Some organizations have pushed this type of analytics computing out to the cloud, as it’s relatively easy to use and doesn’t require huge capital outlays. Others interested in machine learning, continuous intelligence and cognitive processing, however, are more interested in an on-site platform that will perform data analysis faster, providing a more robust competitive advantage.

“The z Systems platform allows for the training and retraining of machine learning models, continuous intelligence gathering and cognitive processing in a looped environment that has many, many benefits.”
–Nick Sardino

“Last year, IBM announced a cloud-based IBM Watson* Machine Learning environment, and people were rightly very excited about it. There are definitely benefits to it,” Sardino notes. “But others have expressed an interest in having an analytics platform that sits right next to their data.”

And that’s the z Systems platform and IBM z* Analytics, along with a number of related machine learning and analytics tools. The mainframe is able to perform complex machine learning and cognitive processing across multiple data sources more quickly and efficiently than other platforms.

“Some of the more sophisticated analytical operations benefit from z Systems processor technology including Java* enhancements that benefit the analytics engine, the IBM z/OS* Platform for Apache Spark, as well as the large amount of supported memory, increased I/O bandwidth and a very rich caching structure,” Sardino says. “This is especially true of the IBM z13*.”

And unlike cloud resources, an on-premises mainframe essentially butts against critical data-storage resources. This helps allay concerns some may have regarding machine learning in the cloud, including how to transfer data, how to ensure cloud-based data is current, how secure the data is and how to avoid potential network latency issues that may hamper model retraining.

“By not moving data off the mainframe, you’re actually preserving security and governance of the data. When you’re not making, moving and sharing copies of it, you can prevent breaches or compromised data,” Sardino says. “It’s also about the value of the data on the mainframe, the value of running analytics on that data and the proximity to up-to-the-minute transactional data.

“The proximity to the data benefits users in a couple of ways: the ability to call a model-based scoring routine from the transaction itself and still meet tight service-level agreements, and the ability to retrain on incoming data when the model accuracy falls below the desired threshold.”

Jim Utsler, IBM Systems Magazine senior writer, has been covering the technology field for more than a decade. Jim can be reached at jjutsler@provide.net.


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