Enterprise Architect Guide to H2O Open Source for Model Building and Deployment

Presented by

Gregory Keys PhD, Senior Solutions Architect at H2O.ai

About this talk

The H2O Open Source Machine Learning Platform (H2O-3) empowers data scientists to train ML models at massive scale (GBs to TBs of training data) using familiar languages and IDEs on existing or greenfield distributed compute environments. Data scientists export a standardized scoring artifact of a trained model and dev-ops teams deploy these as low latency prediction software to diverse production systems (Rest endpoint, RDBMS, Kafka, etc) all via existing SDLC processes. Numerous top companies across verticals have leveraged the power and simplicity of H2O-3 to become innovative AI companies while adhering to strict enterprise security and governance provided by the platform. Let’s learn how. In this webinar, you will learn: - The value of H2O Open Source from the data scientist’s perspective - The value of H2O Open Source from the dev-ops and business owner perspective - Technical architecture of H2O Open Source ML Platform - Enterprise choices and best practices in implementing H2O Open Source on distributed compute for model building - Enterprise choices and best practices in deploying trained models to diverse production software environments - Enterprise security and governance controls of H2O Open Source Presenter: Gregory Keys PhD, Senior Solutions Architect at H2O.ai

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H2O.ai 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.