Make IBM Z part of your AI strategy – build AI models anywhere, deploy in-transaction on IBM Z

Make IBM Z part of your AI strategy – build AI models anywhere deploy on IBM Z

Join this webcast and demo to see how data originating on IBM Z can be incorporated into machine learning models and then readily deployed to IBM Z for in-transaction scoring (inferencing).

Data is key to business success and AI is vital to unlocking the value of that data. As you implement AI, you need to consider the best approach: on premises, in the cloud or a blend of both. You have to decide where to build machine learning models and where to deploy them.

As Forrester states, you should “…build ML/AI models wherever it makes the most sense, but consider scoring close to the transactional application to decrease scoring latency.¹”

End to end model training, deployment and monitoring is available on IBM Z through IBM Watson Machine Learning for z/OS. It gives you the flexibility to train your models in any framework, on any platform, including IBM Cloud Pak for Data, then readily deploy those models on IBM Z to gain the unique advantage of inferencing in-transaction, in-real time.

Through low latency, high performance inferencing you can readily embed models in mainframe applications and score every transaction without significant impact to SLAs. By scoring every transaction as it is taking place, more opportunities can be uncovered and quickly acted on to help increase revenue, decrease cost and mitigate fraud in real-time.

¹ Forrester Thought Leadership Paper October 2020: Leverage Data Where It Originates To Drive Substantial Business Benefits