LLMOps Foundations: Deploying LLMs with Jenkins, Docker, and Kubernetes

Deploying Large Language Models (LLMs) to production requires more than just writing code; it demands robust infrastructure, automation, and continuous monitoring. This text-based course guides you through the core concepts of LLMOps, helping you transition from local AI experiments to scalable, cloud-ready deployments. You will gain a thorough understanding of how to containerize LLM applications, automate deployment pipelines, and maintain model performance in production. By studying real-world deployment patterns, you will learn to manage infrastructure efficiently using industry-standard tools and cloud services. What you'll learn: - Understand the core principles of LLMOps, including model serving, vector databases, and retrieval-augmented generation (RAG) architectures. - Build and package LLM applications using FastAPI and Docker containers for consistent deployment. - Automate delivery workflows with Jenkins CI/CD pipelines to streamline testing and deployment. - Orchestrate containerized AI applications at scale using Kubernetes cluster management. - Configure production monitoring and observability using Prometheus and Grafana to track model latency and health. - Deploy scalable models to cloud environments using AWS and GCP infrastructure. The course starts with foundational definitions of LLMOps and containerization before advancing to pipeline automation, orchestration, and production monitoring. You will progress step-by-step through written explanations, conceptual breakdowns, and practical configuration scenarios. This course is designed for software developers, data scientists, and aspiring MLOps engineers who want to learn production deployment. No prior experience with DevOps or cloud infrastructure is required, as we build up from foundational concepts. Start reading today to master the infrastructure behind modern generative AI applications.