From notebook to production — models that are monitored, costed, and maintained
Forecasting, NLP/CV, personalization — with MLOps, governance, and clear ownership after handover.
Outcomes
- Lift on accuracy/recall vs baseline — proven with holdout tests
- Predictable inference latency and cost per prediction
- Model registry, versioning, and approval workflow
- Monitoring for drift, bias, and service health
What we deliver
- Problem framing & data readiness assessment
- Training pipelines, feature store integration, experiment tracking
- Batch/online serving (APIs, streaming) with autoscaling
- Dashboards, alerts, and runbooks for model ops
FAQs
How much data do we need?
Depends on the task. We often start with heuristics/baselines, then estimate expected lift and data gaps before training.
Who maintains models after launch?
We transition ownership with docs, dashboards, and SLOs; we can also provide ongoing support.