Practical AI: From Model to Deployment
Practical AI: From Model to Deployment Turning a well‑trained model into a reliable service is a different challenge. It needs repeatable steps, clear metrics, and careful handling of real‑world data. This guide shares practical steps you can apply in most teams. Planning and metrics Plan with three questions: what speed and accuracy do users expect? How will you measure success? What triggers a rollback? Define a latency budget (for example, under 200 ms at peak), an error tolerance, and a simple drift alert. Align input validation, data formats, and privacy rules to avoid surprises. Keep a changelog of schema changes to avoid surprises downstream. ...