Big Data Days 2019

 October 8-10   Moscow

Kelly Schlamb

IBM Canada Ltd., Canada


Kelly Schlamb is an Executive IT Specialist in the Cognitive Systems group at IBM. His 24 year career at IBM has included roles in development, technical sales and enablement, where he has focused on database technology, big data, analytics, cloud and most recently artificial intelligence. Kelly is a frequent presenter at conferences and user groups worldwide.


Data Science & AI: Infrastructure Matters

Artificial intelligence is increasingly being seen as a competitive advantage and every company is running fast to try and be a part of this revolution. However, on its own, AI faces a steep time-to-value curve. For example, do you have the right data? Do you have the right skills? Can everyone participate (typically, AI is in the hands of the privileged few and companies struggle to democratize it for the many)? But there is one often overlooked component of a strong AI strategy, which has the effect of flattening out that time-to-value curve for AI: Infrastructure. In a world of algorithms, chat bots and neural networks, infrastructure matters like it has never mattered before. But it’s important to understand that infrastructure is not just hardware. There are components to an AI-optimized infrastructure that are indeed hardware-based; however an optimized AI infrastructure goes beyond just the hardware. It’s about having the capabilities of an enterprise insights platform that handles the end-to-end workflow for AI, right from data collection and organization through to deploying the resulting insights into the business. It’s also about optimizations of the underlying hardware with exploitative software to make it not just run faster, but deliver value faster while at the same time sending an open invitation to the entire origination to come and participate in the AI revolution. In this presentation you’ll hear about how collaborative offerings such as Watson Studio, Watson Machine Learning, Watson Machine Learning Accelerator and IBM Cloud Private for Data can quickly get an organization on the road to AI, as well as how the right hardware infrastructure can further accelerate that journey.

Session Keywords

Data Science Infrastructure