Deploy Resilient AI Microservices with LangChain

Übersicht

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. This course is for developers and MLOps pros ready to scale LangChain apps using Python, APIs, and Docker for production-grade AI systems.

Learners should have basic Python or JavaScript skills, be familiar with REST APIs and Docker fundamentals, and understand general AI or LLM workflows. By the end of this course, you'll have a fully deployed, observable, fault-tolerant microservice architecture with reusable templates, deployment YAMLs, and a resilience checklist for any AI system.

Designed for developers, data engineers, and MLOps professionals ready to make AI systems not just smart, but strong.

Lehrplan

• Building AI Microservices with LangChain
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.
• Containerization, Deployment, and Telemetry
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.
• Ensuring Resilience and Reliability
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.

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Unterrichtet von

Starweaver and Karlis Zars

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Fachgebiete

Artificial Intelligence