What do containers have to do with AI? Linux containers are increasingly popular with application developers: they offer improved elasticity, fault tolerance, and portability between different public and private clouds, along with an unbeatable development workflow. Artificial intelligence and machine learning do not exist in a vacuum. Rather, we put them into production as components of intelligent applications, which learn from data to provide improved functionality to users. William Benton explains how container application frameworks provide profound value to intelligent applications.
William shares his expertise developing and deploying intelligent applications, including model training, serving, stream processing, and ETL, in Linux containers and Kubernetes. He covers what containers are and why you should care about them, whether you’re an application developer, an AI engineer, or a data scientist. You’ll learn the benefits of containerizing applications, contemporary architectures for analytic applications, and how to handle external data sources. You’ll also gain practical advice on how to ensure security and isolation, how to achieve high performance, and how to sidestep and negotiate potential challenges related to deploying JVM-based frameworks like Apache Spark or Apache Flink in resource-constrained environments. Throughout the talk, William offers concrete lessons about containerized analytic jobs ranging from interactive notebooks to production applications. You’ll leave inspired and ready to deploy high-performance intelligent applications without giving up the security you need or the developer-friendly workflow you want.
William Benton is an engineering manager and senior principal software engineer at Red Hat, where he leads a team of data scientists and engineers and has applied machine learning to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His current focus is investigating the best ways to build and deploy intelligent applications in cloud native environments, but he’s also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.
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