A model needs a repeatable path into operation
Getting a model to work in an experiment is an achievement. Keeping a service working as its data, users, software, and business conditions change is another job. Machine-learning operations, or MLOps, connects learning with repeatable releases, monitoring, and recovery. It describes the responsibilities and practices involved in operating the system.
We will build a Production ML Lifecycle Map around two examples: a retailer's fraud model and a document research assistant. The fraud model supports review decisions with predictions. The assistant combines retrieval, generation, and application logic. Both need versions, clear ownership, evidence about performance, and a way to learn from what happens in use.
Review Module 1 for training and evaluation and Module 6 for serving and observability. You can draw the complete workflow on paper before writing code. The important question is where behavior changed and what evidence would help you choose a response.