AI and ML: The Critical Operational Side of Running Applications in Kubernetes

Overview

Discover how to effectively manage AI and ML operations using service mesh, focusing on GPU workloads, multitenancy, and scaling in Kubernetes environments for reliable and observable ML applications.

Syllabus

• Introduction to Kubernetes for AI/ML
Overview of Kubernetes architecture
Key concepts: pods, nodes, and clusters
Kubernetes networking basics
• Understanding AI and ML Workloads in Kubernetes
Characteristics of AI/ML workloads
Common challenges in deploying AI/ML on Kubernetes
Introduction to GPU utilization in Kubernetes
• Service Mesh Fundamentals
Definition and benefits of a service mesh
Overview of popular service mesh technologies (Istio, Linkerd, etc.)
Implementing a service mesh in Kubernetes for AI/ML applications
• Managing GPU Workloads with Kubernetes
Configuring Kubernetes for GPU scheduling
Best practices for GPU resource management
Tools and frameworks for optimizing GPU workload performance
• Multitenancy in Kubernetes
Approaches to achieving multitenancy
Managing namespaces and resource quotas
Security considerations in multitenant environments
• Scaling AI/ML Applications in Kubernetes
Horizontal and vertical pod autoscaling
Load balancing and resilience strategies
Handling stateful vs stateless workloads
• Observability and Monitoring in AI/ML Operations
Setting up monitoring for Kubernetes-based applications
Using tools like Prometheus and Grafana
Implementing logging and tracing
• Case Studies and Best Practices
Real-world examples of AI/ML deployments in Kubernetes
Lessons learned and best practices from industry
• Summary and Q&A
Key takeaways from the course
Open floor for questions and clarifications

Subjects

Programming