Machine Learning Meets Kubernetes - Infrastructure and Orchestration for AI Workloads

Explore the intersection of machine learning and Kubernetes orchestration in this 26-minute conference talk from Conf42 Cloud Native 2025. Begin by understanding the AI revolution's infrastructure demands and how Kubernetes addresses the scalability and management challenges faced in traditional machine learning deployments. Learn about the complete machine learning lifecycle and discover why Kubernetes has become essential for ML operations, from development to production deployment. Dive into core Kubernetes concepts specifically relevant to machine learning workloads, including containerization, orchestration, and resource management. Get introduced to Kubeflow, the machine learning toolkit for Kubernetes, and explore its key components for building, training, and deploying ML models at scale. Master best practices for running machine learning workloads on Kubernetes, including effective resource allocation strategies that optimize compute, memory, and GPU usage. Understand critical security considerations when deploying ML models in containerized environments and examine real-world use cases demonstrating successful ML implementations on Kubernetes. Conclude by exploring emerging trends and future developments in the convergence of machine learning and Kubernetes technologies, gaining insights into how this powerful combination is reshaping modern AI infrastructure and operations.