Deploying machine learning models should be predictable, repeatable, and auditable—not a leap of faith. This talk breaks the deployment lifecycle into clear, actionable steps and demonstrates pragmatic choices for containerization, serverless and platform-based deployments, monitoring, and scaling. It also introduces the Model Context Protocol MCP as a practical pattern for giving deployed models secure, auditable access to external data and tools. Attendees will leave with concrete patterns, a checklist to avoid common pitfalls, and reproducible workflows they can apply the next day to move models from experimentation into reliable production services.