Deploy Resilient AI Microservices with LangChain is a hands-on course that transforms LangChain applications from local prototypes into production-grade systems. You'll decompose monolithic apps into modular services—retrievers, LLM endpoints, and post-processors—connected through gRPC interfaces for scalability and fault isolation. You'll containerize and deploy using Docker and Kubernetes, writing production-ready Dockerfiles with health checks, managing environment variables, and automating rollouts to AWS ECR. Then implement comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and Jaeger/Grafana dashboards to measure latency, throughput, and errors. Finally, you'll master chaos engineering using Chaos Mesh or Gremlin to simulate pod failures, network delays, and resource exhaustion, calculating MTTD and MTTR to measure system resilience.
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Deploy Resilient AI Microservices with LangChain
This course is part of Build Next-Gen LLM Apps with LangChain & LangGraph Specialization
Instructors: Starweaver
Included with
Recommended experience
What you'll learn
Analyze AI workloads to define logical microservice boundaries and implement modular LangChain components communicating via gRPC.
Apply containerization and orchestration using Docker, ECR, K8s to deploy, scale, and monitor LangChain services with health checks and telemetry.
Evaluate and strengthen resilience by implementing OpenTelemetry tracing, Prometheus metrics, and chaos testing to measure and improve recovery.
Skills you'll gain
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December 2025
1 assignment
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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