ML pipelines that hold under load.
A model is 5% of an ML system. We build the other 95% — feature pipelines, reproducible training, deployment automation, monitoring — so your data scientists ship instead of firefighting.
✓ inference cost reduction: up to 68% · uptime: 99.9%
✓ ip transfer: complete · lock-in: none
✓ delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
Models decay. Pipelines break. Most teams find out from users.
The median enterprise model takes months to reach production and degrades silently once there. The cause is rarely the model — it's missing infrastructure: no feature lineage, no retraining triggers, no drift detection, manual deployments. MLOps is the difference between a model and an asset.
Capabilities
Feature & data pipelines
Versioned, tested, monitored feature pipelines with lineage. Training-serving skew eliminated by construction, not by debugging.
Training infrastructure
Reproducible training with experiment tracking, hyperparameter management, and spot-instance orchestration that cuts compute bills.
Deployment automation
Models ship through CI/CD with automated validation gates — shadow deployment, A/B rollout, instant rollback.
Drift & quality monitoring
Input drift, prediction drift, and performance decay detected and alerted before users notice. Retraining triggers wired to thresholds.
Inference optimization
Quantization, batching, distillation, and right-sized serving. We've cut inference bills by 68% without measurable quality loss.
Platform & team enablement
We build the platform and train your team on it. The goal is your independence, not our retainer.
The approach
A sequence, because the order is the point: each phase gates the next on evidence.
Audit
Two weeks mapping your current path from data to prediction: where time goes, where failures hide, what breaks at 10x scale.
Platform foundations
Feature store, experiment tracking, model registry, CI/CD skeleton — the rails everything else runs on.
Migrate & automate
Existing models moved onto the platform with validation gates, monitoring, and automated retraining where it pays for itself.
Optimize & transfer
Inference cost tuning, load testing, runbooks, and structured team handover.
Deliverables
- End-to-end ML platform (IaC, fully owned)
- Feature pipelines with lineage and tests
- CI/CD with automated model validation gates
- Drift monitoring and alerting stack
- Inference cost optimization report
- Experiment tracking and model registry
- Retraining automation where justified
- Runbooks + team training program