← All services // AI/ML OPS
Make your ML models survive production. Drift, retrain, monitor, govern.
AI/ML lifecycle services covering data collection and labeling, model training, deployment, monitoring, governance, drift detection and automated retraining. We bring data scientists, ML engineers and DevOps together so models actually work — and keep working — 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
// WHAT WE DO
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)
// HOW WE DO IT
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.
// TOOLS WE USE
Industry-standard. No exotic choices.
MLflowKubeflowFeastBentoMLTorchServeSageMakerVertex AIEvidentlyArize
// COMMON QUESTIONS
Questions clients ask before signing.
- Do you do model training too?
- For deployment work, yes. For full research-grade model development, see our AI/ML Development service.
// RELATED SEARCHES
Ready to talk?
30 minutes is enough to know if we're a fit. Bring your messiest problem.