Customer churn prediction system
What buyers should infer
Shows a production-style pattern: one pinned model artefact, fixed probability and tier outputs, and an HTTP surface your stack can call—without silent notebook drift on the live path.
Commercial fit
This is the storefront ML APIs & Serving pattern in miniature: artefacts you freeze, payloads you regulate, checkpoints you rehearse—with your corpus, SLA expectations, and retrain milestones formalised separately when you graduate from the reference demo.
Reference overview
Per-account churn probability, tier, and routing flag are served from a single frozen artefact and agreed cutoffs. Live inference never depends on ad-hoc notebook runs; training and ad-hoc exploration stay off the request path by design.
Handoff notes
Public deploy reflects an end-to-end reference stack (model package, API, TLS, health checks). The bundled dataset illustrates the architecture only—your engagement would use your data, written acceptance checks, and separate milestones for retrain, drift review, or always-on observability.