What you'll be able to do- ✓Decompose a working in-process recommender into four services and an orchestrator
- ✓Decide where a service boundary belongs by asking what changes on a different cadence, not what looks like a different function
- ✓Define the contract each service exposes, and identify what the contract cannot express
- ✓Build a candidate-generation service that loads a model artifact at startup and serves top-N over HTTP
- ✓Build an orchestrator that calls the services in order, holds each call to the latency budget, and sets an explicit policy for a stage that fails
- ✓Write the batch job that produces the artifacts the serving path reads
- ✓Prove your five-process system reproduces the recommendations the same code produced in one process
Turn a trained model into the thing a company actually runs — a set of services with contracts, a latency budget, and a failure policy. This is the step between a notebook that works and a system that serves: where the model's work ends and engineering starts. Wraps a retrieval model; do Build a Two-Tower Retrieval Engine first if two-tower models are new to you.
⌁ Every production recommender is this shape: retrieval, features, eligibility and ranking behind an orchestrator, with a batch job feeding them. The same decomposition carries to search ranking, feed ranking, and retrieval-augmented LLM systems.