Obtain buy-in from key stakeholders and gather data. Define a use case for implementing artificial intelligence (AI) and create objectives around that. Devise a plan to integrate your data sources with existing infrastructure. Tune and optimize your model to best meet the needs of your organization. Determine how to leverage AI insights in your organization.

Always nice when I get an opportunity to do an iTalk with one of my old friends. And for all that―this is one of my old friends. This is actually the first iTalk that we've done together. So hello―or should I say bonjour―or sorry no, should I say boungiorno to Pascal Polverini. So before we start Pascal, for anybody who may not have come across paths with you before, Pascal is a Frenchman who has been living in Italy for very many―for many years.

Machine Learning Applications The recent announcement of IBM Machine Learning on z/OS is significant for a couple of reasons. Beyond that, it's important for anyone who supports software installation, database administration and analytics on z/OS. Studies have shown that 70 percent of the world's data being accessed for analytics originates on the mainframe. While one of the traditional reasons for moving the data off z/OS has been to avoid impacting operational online transaction processing (OLTP) performance with resource intensive online analytic processing (OLAP), over the past few years DB2 for z/OS has evolved into a hybrid transactional/analytical processing (HTAP) database. DB2 for z/OS--combined with IBM DB2 Analytics Accelerator--form a self-managing database management system that maximizes the efficiency of each workload it runs.