Models that get better after launch.
Serving, monitoring and continuous retraining — the machinery that keeps ML honest in production.
The model shipped eighteen months ago. It hasn't been retrained since, nobody is measuring drift, and the person who built it has left. Accuracy is quietly decaying, and the first stakeholder to notice will be a customer.
- Model serving containerised serving on Cloud Run or Vertex AI with canary rollout and automatic rollback; new versions earn traffic, they don't get it by default.
- Prediction and outcome logging every inference recorded, outcomes labelled automatically as reality arrives. Without this, retraining is guesswork. It's the first thing we build.
- Continuous retraining scheduled and drift-triggered retraining from live data, with time-based splits (never random — that's how leakage happens in time-series systems).
- Evaluation-gated promotion a candidate model must beat the champion on recent data before it serves a single request. No gate, no promotion.
- Drift and performance monitoring feature drift, calibration, hit rates — on dashboards your team reads weekly, with alerts that fire before the business notices.
- Weeks 1–2 — Review Your models, their serving path, and what's actually measured. Output: a prioritised gap list — usually shorter than feared, always more urgent.
- Weeks 3–8 — Build The loop, end to end: logging → monitoring → retraining → gated promotion, running in your cloud on one model that matters.
- Handover The pattern is templated; your team applies it to the rest of the estate.
On Vodafone's Neuron platform we built the self-service execution layer that served 88 ML models in production across 11 markets. The same machinery — evaluation-gated promotion, canary rollout, continuous retraining from real-world execution data — is what Urekaa.ai, our stock-analysis product, will run on at launch, currently in pre-launch.
Vertex AI · Cloud Run · BigQuery ML · Dataflow · Model Registry · Docker · Terraform · Python · Cloud Scheduler · drift monitoring (PSI, calibration)
One question tells us everything: when was your model last retrained?
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