ML & Data Science

Machine Learning That Ships to Production

From Isolation Forest ensembles to fine-tuned foundation models — we take models from notebook to production with full observability, A/B testing, and automated retraining.

Typical engagement starts when

• a model concept looks promising, but the team needs a production path with monitoring, rollback, and evaluation before launch
• anomaly detection, ranking, or classification is affecting live workflows and the current heuristics are no longer holding up
• the organization has enough data and product pressure to justify ML, but not enough operational rigor around training and serving yet
• leadership needs to know whether this is truly a model problem, a retrieval problem, or a rules problem before more effort compounds

What We Build

When to Use This

Engineering Standards

• Model versioning and experiment tracking via MLflow
• A/B testing infrastructure for model rollouts
• Automated retraining triggers based on data drift detection
• Production monitoring with Prometheus and Grafana

These controls matter because ML systems usually fail at the operational layer first: no clear rollback, no drift visibility, and no agreement on when a model should stop serving production traffic.

Common failure patterns we fix

• teams fine-tuning or retraining models before proving the data, labeling, or evaluation setup is strong enough
• promising notebook results with no production path for rollback, observability, or safe rollout
• models serving live traffic without drift detection, threshold review, or clear ownership when quality degrades
• ML introduced where retrieval, rules, or product changes would solve the problem more simply
• recommendation or anomaly systems tuned for offline metrics while production feedback loops stay weak or invisible

What you leave with

• an ML architecture matched to the real business signal and production operating constraints
• evaluation, rollout, and monitoring criteria that make model changes governable instead of subjective
• serving, retraining, and rollback paths the internal team can operate without guessing
• a clearer decision on where ML belongs in the system and where deterministic logic should still win

Best Fit

• Team already has enough data volume, signal quality, and operational need to justify production ML
• Use case depends on anomaly detection, ranking, classification, or domain-specific model performance
• Engineering leadership wants experiment tracking, versioning, monitoring, and rollback handled as part of the system
• Model outputs affect live product behavior, risk scoring, or analyst workflows and therefore need production discipline

Specialist Capabilities