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Deploy Resilient AI Microservices with LangChain

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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

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

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This course is part of the Build Next-Gen LLM Apps with LangChain & LangGraph Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

Module details

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

4 videosTotal 26 minutes

Welcome to Building AI Microservices with LangChain3 minutes
The LangChain Microservice Mindset6 minutes
Breaking Down the Chain: Defining Service Boundaries7 minutes
Demo: Building Your First gRPC LangChain Service10 minutes

2 readingsTotal 10 minutes

Welcome to the Course: Course Overview5 minutes
What is Microservices Architecture: Google Cloud Learn Guide5 minutes

1 peer reviewTotal 20 minutes

Hands-On-Learning: Split the Chain - Design and Deploy Your First LangChain Service20 minutes

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

3 videosTotal 23 minutes

From Local to Cloud: Dockerizing LangChain7 minutes
Kubernetes for AI: Deploy, Scale & Monitor9 minutes
Demo: Telemetry in Action - Tracing & Metrics with OpenTelemetry + Prometheus7 minutes

1 readingTotal 5 minutes

Kubernetes Basics - Google Cloud Learn Guide5 minutes

1 peer reviewTotal 20 minutes

Hands-On-Learning: Deploy and Monitor Your First LangChain Service20 minutes

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.