Deploying Open Models and LLMs

Open-source models offer unmatched privacy, customization, and cost control, but hosting them yourself requires a solid understanding of deployment pipelines. This text-based course guides you through the foundational concepts and practical steps needed to take open-weight models to production. You will transition from using closed-source APIs to serving, managing, and scaling your own open models, giving you full control over your AI infrastructure. In this course, you will: 1. Understand the foundational architecture of open-weight models and how they differ from proprietary APIs. 2. Configure local and cloud environments to run models efficiently using modern containerization tools. 3. Build production-ready API endpoints around open models using lightweight web frameworks. 4. Apply optimization techniques such as quantization to run large models on limited hardware. 5. Implement basic Retrieval-Augmented Generation patterns to connect your deployed models to external data. 6. Monitor model performance, latency, and resource consumption. The course starts with essential terminology and hardware requirements before moving into step-by-step written explanations, code snippets, and optimization strategies. This course is designed for software developers, engineers, and technical creators who are new to machine learning deployment, with no prior DevOps or AI engineering experience required. Start building your self-hosted AI pipeline today.