Considerations for Deploying AI and Machine Learning in the Cloud

Presented by

Vinod Iyengar,

About this talk

As the world is moving towards cloud deployments, enterprises of all sizes are trying to figure out the best ways to optimize their workloads using the available set of resources. This often involves evaluating their portfolio of workloads and applications and identifying the best cloud or non-cloud venue to host each. The decision process is based on multiple considerations, including performance, integration issues, economics, competitive differentiation, solution maturity, risk tolerance, regulatory compliance considerations, skills availability, and partner landscape. We'll talk about all the above and some practical ideas on how to go about such a journey specifically from an AI and ML perspective. Finally, we'll also look at a few example deployments with H2O, Sparkling Water, and Driverless AI.

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Subscribers (19201) is the maker of H2O, the world's best machine learning platform and Driverless AI, which automates machine learning. H2O is used by over 200,000 data scientists and more than 18,000 organizations globally. H2O Driverless AI does auto feature engineering and can achieve 40x speed-ups on GPUs.