Kubernetes AI Edge Deployment Patterns - Choosing the Best for Your Use Case

Explore the challenges and decisions faced by Machine Learning Operations (MLOps) Engineers focused on AI at the edge in this informative conference talk. Discover solutions for handling distributed architectures across hybrid environments, managing data produced at the edge, dealing with old devices running critical analytics, and managing disconnected far edge deployments. Learn how to select the best deployment patterns for AI at the edge using Kubernetes and Open Data Hub. Watch a live demo showcasing MLOps pipelines, Open Cluster Management, and OpenTelemetry to deploy a model to the edge and gather, store, and forward key metrics to the central hub. Gain valuable insights to help answer crucial questions and make informed decisions for your specific industry and use case.

How to Choose the Best Kubernetes AI Edge Deployment Patterns for Your Use Case