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LLMOps: Building Production-Ready LLM Systems
Learn LLMOps end-to-end by building a real LLM application. You’ll test it, secure it, and iterate on it over time so it stays reliable, safe, and performant in production.
LLMOps is the practice of keeping an LLM application reliable under production traffic, within cost limits, and in the face of security threats. In this course, you’ll learn LLMOps by building and operating an application from the ground up with production constraints in mind.
You’ll begin with the shift from classical ML to foundation models and the constraints that drove LLMOps: stochastic outputs, high inference costs, and new operational artifacts like prompts and vector indexes. You’ll apply the 4D LLMOps life cycle to define quality gates that prevent the project from stalling at the proof-of-concept stage.
You’ll implement a reference RAG architecture, and validate retrieval using golden datasets. Next, you’ll version prompts, enforce structured outputs, and add automated evaluation with LLM-as-a-judge patterns and regression tests. Finally, you’ll prepare for production with security and compliance controls, containerized deployment, and feedback loops to keep quality improving after launch.
LLMOps is the practice of keeping an LLM application reliable under production traffic, within cost limits, and in the face of s...Show More
WHAT YOU'LL LEARN
A clear understanding of what LLMOps means and how it is different from MLOps when working with large language models
Hands-on practice building an LLM app architecture with separate ingestion and inference pipelines
Strong skills in RAG, including chunking text, creating embeddings, storing vectors, and checking results with a golden dataset
The ability to manage prompts as versioned system artifacts, enforce strict output formats, and reduce prompt injection risk through structured prompting patterns
Working knowledge of LLM evaluation, including LLM-as-a-judge scoring, repeatable tests, and using human feedback to improve answers
Hands-on experience in production hardening, including OWASP-aligned security controls, deployment using containerization, and capacity planning for cost and latency
A clear understanding of what LLMOps means and how it is different from MLOps when working with large language models
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Learning Roadmap
1.
The Evolution of Modern AI Systems
The Evolution of Modern AI Systems
Establish the theoretical and historical groundwork for LLMOps, defining why the discipline exists and how it diverges from traditional MLOps.
2.
LLMOps Core Concepts
LLMOps Core Concepts
Define the course’s structural frameworks, introducing the 4D life cycle for process management and a reference architecture for building scalable RAG apps.
3.
Phase 1: Discover and Data Engineering
Phase 1: Discover and Data Engineering
3 Lessons
3 Lessons
Execute the discovery phase by scoping the course project and building data engineering pipelines to transform raw data into retrieval-ready assets.
4.
Phase 2: Distill and The Core Engine
Phase 2: Distill and The Core Engine
2 Lessons
2 Lessons
Execute the distill phase by constructing the core RAG components for retrieval and generation. Explore how to implement automated evaluation gates.
5.
Phase 3: Deploy and Hardening
Phase 3: Deploy and Hardening
3 Lessons
3 Lessons
Execute the deploy phase by hardening the prototype into a production service, focusing on security, infrastructure sizing, and retrieval optimization.
6.
Phase 4: Deliver and Evolution
Phase 4: Deliver and Evolution
3 Lessons
3 Lessons
Execute the deliver phase by adding conversational state, implementing feedback loops for continuous improvement, and exploring the future of AI agents.
Certificate of Completion
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