Design Patterns for Running AI/ML and Big Data Workloads on Kubernetes

Learn how to effectively deploy AI/ML and Big Data workloads on Kubernetes through a 32-minute technical talk that explores emerging design patterns and solutions for common challenges. Discover strategies for managing container placement, configurations, confidential data, governance policies, metadata, shuffle data, logging, monitoring, and autoscaling in a Kubernetes environment. Explore practical approaches for sharing data and libraries between containers while running engines like Spark, Flink, Hive, Tez, and Flume. Master the architectural patterns that have evolved to address the unique requirements of deploying analytical applications and machine learning workloads in cloud-native environments.