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.
Chris Fregly is a senior developer advocate focused on AI and machine learning at Amazon Web Services (AWS). Chris shares knowledge with fellow developers and data scientists through his Advanced Kubeflow AI Meetup and regularly speaks at AI and ML conferences across the globe. Previously, Chris was a founder at PipelineAI, where he worked with many startups and enterprises to deploy machine learning pipelines using many open source and AWS products including Kubeflow, Amazon EKS, and Amazon SageMaker.
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