LLMOps is a comprehensive, hands-on course designed to help you master the complete lifecycle of Large Language Model (LLM) applications, from development and evaluation to deployment and production monitoring. You will learn why LLM applications require specialized operational practices and how LLMOps differs from traditional MLOps, particularly when dealing with non-deterministic outputs, evolving prompts, and LLM-based evaluation.

Starting with the fundamentals of Retrieval-Augmented Generation (RAG), you will build a RAG application using LangChain, LangGraph, and Chroma. You will gradually transform this baseline application into a production-ready system by learning to structure projects effectively, version prompts with Langfuse, and evaluate application performance using golden datasets, DeepEval, and LLM-as-judge metrics.

You will explore experiment tracking with MLflow and DagsHub, learning to track parameters, prompts, datasets, code, and evaluation metrics. The course also covers statistical thresholds, regression testing, promotion testing, and the champion-challenger approach to ensure that application improvements are measurable and reliable. Additionally, you will implement guardrails using Guardrails AI to validate user inputs, retrieved context, and generated outputs.

Moving beyond evaluation and testing, you will learn to serve LLM applications using FastAPI and Streamlit, automate testing through GitHub Actions, and containerize applications using Docker and Docker Compose. You will then build CI/CD pipelines to deploy applications to AWS using Amazon ECR, Amazon EC2, and AWS CodeDeploy, while exploring health checks, load balancing, scaling, and rollbacks.

The course also dives into production observability, teaching you to trace LLM application execution with Langfuse, evaluate live production traffic, and use real-world insights to improve application performance. You will learn infrastructure monitoring using Node Exporter, Prometheus, and Grafana to track server metrics and visualize system health.

As a capstone project, you will apply the complete LLMOps lifecycle to an agentic application built with LangGraph. From prompt versioning and agent evaluation to guardrails, automated testing, deployment, tracing, and monitoring, you will implement the same operational practices for an agentic system, including multi-step workflows and tool-use scenarios.

By the end of the course, you will be equipped to build, evaluate, test, deploy, and monitor reliable LLM applications, applying production-oriented engineering practices across both RAG-based and agentic AI systems.

Course Duration: 57+ hours
Refund Policy: 4 days