Unlock the potential of your data: Optimize your data store architecture for AI/ML and Analytics

AI/ML and analytics applications are extremely demanding in terms of data store solutions as they require a broad spectrum of storage capabilities to support a wide spectrum of workloads, from continuous data ingest streams to data transformation and to model training, inferencing and archival. Each of these workloads have distinct throughput, latency, capacity and scalability requirements that have to be satisfied. Next-gen storage architecture must combine the high-performance and storage-density optimized industry standard servers with Software-Defined-Storage (SDS) to provide high-performance, distributed, modular, cost effective and petabyte scalable architectures, capable of satisfying all AI and Analytic requirements.

Who is this presentation for?
IT manager, Data scientists and analysts, Big data data architect
 

Prerequisite knowledge:
None
 

What you'll learn?
How storage impact AI and how to create optimal data architecture for AI/ML

Andrea Fabrizi, M.S. in Physics, is an expert in big data and analytics at HPE Storage and Big Data Business Unit. He comes from a long career in big data, analytics, product management and telecommunication. In the last years, Andrea has worked as analytic product manager for several large US corporations. Prior, he was responsible of the product management of analytics products and solutions for telecommunication market at both Capgemini USA and HP. Andrea has been lecturer at University of Washington and speaker and panelist at several leading industry events such Mobile World Congress (MWC) and Telco Big Data Summit.