Make your ML models survive production. Drift, retrain, monitor, govern.
Data labeling, model training pipelines, deployment to inference endpoints, monitoring and retraining. We make ML reliable in production.
Right for you if
- ✓ Already have a model in production (or about to deploy one)
- ✓ Predictions affect real customer/revenue decisions
- ✓ Need governance and audit trails
Probably not right if
- — Still in research / Jupyter-only stage — see AI/ML Development first
Concrete deliverables, not buzzword soup.
- Feature stores (Feast, Tecton)
- Model registry, versioning, A/B testing
- Automated retraining pipelines
- Model drift and data drift detection
- Inference serving (Triton, TorchServe, BentoML, SageMaker)
- ML observability (Arize, WhyLabs, Evidently)
Three steps. Two-week sprints. Weekly demos.
- 01
Production-readiness audit
How fragile is your current model? What breaks first?
- 02
Pipeline + serving stack
Train → register → deploy → monitor as one repeatable loop.
- 03
On-call handoff
Your data scientists become productive in production, not just notebooks.
Industry-standard. No exotic choices.
MLflowKubeflowFeastBentoMLTorchServeSageMakerVertex AIEvidentlyArize
Common questions
- Do you do model training too?
- For deployment work, yes. For full research-grade model development, see our AI/ML Development service.
Related services
Ready to talk?
30 minutes is enough to know if we're a fit. Bring your messiest problem.