Cloud-Native ML Infrastructure: Building Resilient Apache Spark Clusters on Kubernetes for AI Workloads

Abstract

The convergence of AI/ML workloads with cloud-native infrastructure presents unique challenges in scalability, resource utilization, and operational complexity. This talk demonstrates how to architect production-grade Apache Spark clusters on Kubernetes that specifically cater to the demands of modern AI/ML applications while adhering to cloud-native principles.

Key Topics

• Implementing cloud-native patterns for Spark on Kubernetes: statelessness, declarative configurations, and immutable infrastructure
• Designing resilient architectures for ML workloads using Kubernetes operators and custom controllers
• Container optimization strategies for Spark executors running AI/ML workloads
• Network optimization and storage patterns for large-scale data processing
• Implementing GitOps workflows for Spark-based ML infrastructure
• Monitoring and observability solutions for distributed ML training

Attendees will gain practical insights into building cloud-native data infrastructure that scales effectively for AI/ML workloads, with real-world examples of Kubernetes configurations, deployment patterns, and operational best practices.