This module lays the groundwork for transforming LangChain applications into modular, scalable microservices. You’ll analyze AI workloads to identify natural boundaries-retriever, model, post-processor-and design gRPC interfaces for each. Through hands-on demos, you’ll implement your first LangChain microservice, test its endpoints locally, and visualize how traffic flows between components. By the end, you’ll have a clear understanding of how to split, structure, and connect LangChain logic for cloud deployment.

This module takes your LangChain microservices from local code to production-grade deployment. You’ll package components into Docker images, push them to AWS ECR, and orchestrate them in Kubernetes with health checks and scaling policies. Once deployed, you’ll integrate OpenTelemetry tracing and Prometheus metrics to monitor latency, throughput, and reliability. By the end, you’ll not only have your service running in the cloud-but also fully observable and ready for load.

This module is all about testing how your system behaves when things go wrong-and proving it can recover. You’ll introduce failure intentionally using Chaos Mesh or Gremlin, simulating pod crashes, network latency, and resource loss. Then, you’ll capture and interpret resilience metrics such as mean time to detect (MTTD) and mean time to recover (MTTR). By the end, you’ll document how your LangChain services withstand disruptions and learn to design architectures that fail gracefully and self-heal.