AI Orchestration: From local models to cloud

Learn to orchestrate AI systems across local and cloud environments through hands-on infrastructure setup, model deployment, and workflow integration. You will build a prompt engineering pyramid from basic prompts to chain-of-thought reasoning implemented in Rust, then evaluate six decision factors for choosing between local and cloud models including latency, throughput, cost, and privacy. The course covers local AI infrastructure in depth: running Ollama with custom Modelfiles for task-specific assistants, deploying llamafile for zero-dependency portable inference, compiling Rust Candle with CUDA for GPU-accelerated local inference, and optimizing local RAG with caching strategies. You will configure a complete AI workstation with tmux for session management, nvidia-smi and Zenith for GPU monitoring, and NVIDIA GPU optimization. The final module covers cloud workflows including AWS Spot instances for cost-effective GPU compute, Hugging Face model discovery and download, and GitHub AI models integration. By completing this course, you will be able to set up local AI infrastructure, deploy models across local and cloud environments, and design orchestration workflows that balance cost, privacy, and performance.