Run LLMs with Docker Model Runner - No Python, PyTorch, or CUDA Required

Learn to deploy Large Language Models locally using Docker Model Runner, eliminating the complexity of Python dependencies, CUDA versions, and PyTorch compatibility issues. Discover how Docker containers solve dependency hell by treating AI models as OCI artifacts, enabling consistent deployment across different machines and environments. Explore inference engines and their importance in AI model deployment, then gain hands-on experience through practical tasks including installing the Docker Model plugin, pulling AI models as containerized artifacts, testing models interactively, and starting background inference services. Master querying models via OpenAI API, creating custom AI personas with system prompts, and packaging models for offline or air-gapped deployments. Understand the DevOps and MLOps benefits of containerized AI model deployment, making this approach ideal for DevOps engineers, ML engineers, data scientists, and developers seeking streamlined AI model deployment solutions without traditional machine learning library dependencies.