Big Data and Machine Learning
How to Prepare for—and Leverage—Machine Learning in the Data Center
Machine learning, a type of AI that enables computers to learn without explicit programming, can save time and money and increase affinity among customers (internal and external). So it’s no wonder that more and more companies are identifying the need to apply and support machine learning for more and more use cases. In this think-tank meeting, attendees will collaborate to determine the requirements for effective support of machine learning for the business--from developing the applications to hosting them at scale--on premise and in the cloud. Attendees will also collaborate on a best practices guide for leveraging machine learning to optimize compute, storage and network resources in the data center itself. Finally, attendees will discuss how to lay plans for bridging the gap between machine learning and the AI of the future
Participant created final content:
Whitepaper: Design and Architecture Best Practices and Requirements for Machine Learning/AI-Ready Data Centers
Participating Companies: University of Minnesota, FastBridge Learning, C.H. Robinson Worldwide, Health Partners and others.
Lab Advisor and Featured Speaker
Kenneth Sanford, Analytics Architect, Dataiku
Kenneth Sanford is the US lead Analytics Architect for Dataiku. He is a reformed academic economist who likes to empower people to solve problems with data. Ken’s primary passion is teaching and explaining. He likes to simplify and tell stories. Ken has spent time in academia (Middle Tennessee State University, U of Cincinnati, Boston College) consulting (Deloitte) and software development (SAS, H2O). He has a Ph.D. in Economics from the University of Kentucky in Lexington and his work on price optimization has been published in peer-reviewed journals. Ken is a veteran of the machine learning and production analytics space and helps people to build and deploy advanced analytics data products.
Live Meeting Details
Attend the Minneapolis Leaders Lab