Open Source LLMOps: Building and Deploying AI Applications

Organizations are increasingly moving toward open-source AI to maintain control over their data, privacy, and infrastructure. This course bridges the gap between understanding how Large Language Models (LLMs) work and actually deploying them in a professional environment using a code-first approach. You will transition from a beginner to a practitioner capable of managing the entire lifecycle of an open-source model. By reading through detailed technical explanations and analyzing code implementations, you will learn to navigate the ecosystem of open-source architectures, optimize them for performance, and integrate them into functional software applications. What you'll learn: - Understand the core architecture of transformers and modern sparse expert models - Implement Retrieval-Augmented Generation (RAG) patterns to connect models to private data - Apply model quantization techniques to run high-performance LLMs on accessible hardware - Configure open-source orchestration tools to manage model deployment and scaling - Practice prompt engineering and fine-tuning strategies to improve model output quality - Build secure and efficient API layers to serve AI models to end-users The course begins with foundational definitions and the history of language modeling before moving into practical, written walkthroughs of LLMOps workflows and deployment strategies. Each module focuses on clear text-based explanations and code snippets that you can apply immediately. This course is designed for developers, data enthusiasts, and technical curious minds who are new to AI operations. No prior experience with large language models is required. Start building your own independent AI infrastructure today.