Deploy Spark ML TensorFlow AI models from notebooks to hybrid clouds (including GPUs)

Chris Fregly explores an often-overlooked area of machine learning and artificial intelligencethe real-time, end-user-facing "serving layer in hybrid-cloud and on-premises deployment environmentsand shares a production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.

Chris Fregly explores an often-overlooked area of machine learning and artificial intelligence—the real-time, end-user-facing “serving” layer in hybrid-cloud and on-premises deployment environments. Serving models to end users in real time in a highly scalable, fault-tolerant manner requires an understanding of not only machine learning fundamentals but also distributed systems and scalable microservices. Drawing on his time at both Databricks and Netflix, Chris shares a 100% open source, real-world, hybrid-cloud, on-premises, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.