AI and Data Science Master the art of data science. Join now
Learn more and register for the May 18 webcast and demo! A study by VentureBeat AI, determined that only an estimated 13% of data science projects have been deployed into production applications. Developing machine learning models is only part of the AI challenge. Leveraging those models to improve business outcomes is important as well. Finally, generating a return on your AI investment is crucial.Perhaps no applications have as much potential to influence business outcomes as your core mainframe (IBM Z) transactional applications. But, can you really integrate AI within your high performance, SLA sensitive transactional applications?
Join IBM for a webcast and demo on May 18th to learn how you can incorporate your business critical IBM Z mainframe data into machine learning models. Through low latency, high performance scoring (inferencing) you can then readily embed those models in mainframe applications and score every transaction without significant impact to SLAs. By scoring every transaction as it is taking place, more business opportunities can be uncovered and quickly acted on to help your organization increase revenue, decrease cost and mitigate fraud in real-time.