The NVIDIA GPU Technology Conference is one of the premier AI events in the world. GTC is a chance to hear from the technologists, practitioners, and visionaries in AI. This year is no different. There are multiple tracks on technology, autonomous vehicles, life sciences, and natural language processing.
For the IBM Storage session, we decided to dig deep into the challenges and best practices for scaling AI adoption in the enterprise. The modern enterprise is a data-rich environment with a variety of data sources of various vintages. Applying NVIDIA GPU-accelerated data analytics on NVIDIA DGX systems can bring client insights, lower costs, streamline operations, and improve customer satisfaction. However, this is dependent on collecting, organizing, and maintaining the data sets needed. In addition, shifting global regulatory and privacy practices demand on-going intelligent (sometimes AI driven) governance of enterprise data sets.
Few companies have embraced AI at the scale of IBM. IBM has been leading the way in enterprise enablement of AI and deep analytics to extract business driven insights from the data they have today. As a global portfolio business, IBM has a breadth of data and a myriad of applications that can benefit from deeper analysis and AI driven insights. These include supply chain, contracts, logistics, manufacturing, sales, and support.
For this GTC session, I called on a friend and colleague, Dr. Steven Eliuk, VP, AI and Governance Automation, to join me in a discussion about what he and the IBM Chief Data Office have learned in the process of infusing AI into all aspects of the IBM business. For the past several years, Steve has been in the trenches of using deep learning across all of IBM’s business. His team contributed to building a data lake of trillions of records and hundreds of datasets that are used by over 500 data scientists, supporting thousands of daily users. His team has developed internal tools that are also now part of the IBM Watson portfolio.
In our IBM GTC session, we will talk about the challenges his team has addressed, and how the choice of tools and infrastructure and partnership with the IT department has helped his team grow. We cover the stages of AI adoption and the critical value of metadata to provide visibility into the data used every day. Steve and I cover the business drivers, technical challenges, tools developed internally, and the best practices for scaling AI in the enterprise. This session is recommended for data officers, data scientists, and IT departments to unite behind the common problems that each group can bring to AI adoption in the enterprise.
As with many sessions in the past year, we are delivering SS33321, Working Together: Four things a data scientist should demand from enterprise IT from our respective homes. However, there are numerous ways to further engage with IBM’s Chief Data Office or IBM Storage on these subjects. IBM THINK will be coming up in May, and the IBM Chief Data Office Summit was held online at https://cdoclub.com/ibm-cdo-summit-series-webinar-registration/.
Join us for Working Together: Four things a data scientist should demand from enterprise IT at https://gtc21.event.nvidia.com/media/Working%20Together%3A%20Four%20Things%20a%20Data%20[…]%20IT%20(Presented%20by%20IBM)%20%5BSS33231%5D/1_edr72v56