AI growing pains: Platform considerations for moving from POC to large-scale deployments (sponsored by Dell Technologies)
As machine learning and deep learning techniques reach mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly as you mature beyond small-scale sandbox and proof-of-concept (POC) environments. You need to ensure I/O bottlenecks are eliminated to keep your GPU-powered AI rocket ship fueled with data; you need to address the issues of data gravity, data scaling, and data economics to support petabyte-sized datasets; and you need to simplify data management and minimize business risk and lifecycle costs of large-scale AI platforms.
Thomas Henson addresses these quandaries, draws key business and architectural requirements for compute and storage, and discusses how enterprises can achieve the maximum benefit from AI platforms that align with these requirements. And he introduces the Dell EMC and NVIDIA solution portfolio, which makes AI simple, flexible, and accessible for organizations looking to deploy large-scale machine learning and deep learning.
This session is sponsored by Dell Technologies.
What you'll learn
- Learn how to effectively use AI as you scale up your business
Thomas Henson is a data engineering advocate and senior systems engineer for the unstructured data solutions team at Dell EMC. Thomas has been involved in many different big data, analytics, and artificial intelligence projects throughout his career, with a focus on distributed systems. He’s a proud alumnus of the University of North Alabama, where he earned his undergraduate and graduate degree in computer information systems. Thomas is an accomplished speaker in the artificial intelligence and big data ecosystem at various conferences.
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