Distributed Tracing Explained Jaeger & Tempo in AI Pipelines Kubernetes Observability video

Description

In modern AI systems, understanding what happens inside complex pipelines is critical. This video explains how distributed tracing using Jaeger or Tempo gives complete visibility across your workflow — from Kafka message consumption to orchestration, specialist agents, model inference, and final result publishing. You’ll learn how tracing helps identify bottlenecks, debug failures, and optimize performance in Kubernetes-based AI architectures. Whether you're working with microservices, GPUs, or LLM pipelines, this observability approach ensures reliability and efficiency at scale. This is a must-watch for developers, DevOps engineers, and anyone building production-grade AI systems. #DistributedTracing #Jaeger #GrafanaTempo #Kubernetes #Observability #DevOps #AIInfrastructure #Microservices #OpenTelemetry #CloudComputing #Kafka #SRE #Monitoring #LLM #vLLM