From notebook to mission.
Logistic regression, SVM, and Bayesian scoring deployed as AWS EKS microservices with end-to-end DevSecOps, models that learn the boundary, then ship and hold it in production.
Science that survives contact with production.
The model is the easy half. The pipeline that trains, tests, deploys, and watches it, inside a federal security boundary, is the capability.
Applied data science
Feature engineering and model selection against real mission data, logistic regression, SVM, and Bayesian scoring chosen for the problem, not the fashion.
MLOps pipeline
Models packaged as EKS microservices with CI/CD, versioned in a registry, and promoted by evidence, the same DevSecOps rigor as any other service.
Evaluation & drift
Champion–challenger evaluation, anomaly detection on inputs, and drift monitoring that triggers retraining before accuracy quietly decays.
Train, ship, watch, retrain.
Frame the mission question
Start from the decision the model informs, who to route, what to flag, when to surge, and define success in mission terms.
Train against real data
Models learn on governed, production-grade data, not a sanitized sample, with validation the ISSO and the analyst both trust.
Ship as a microservice
The trained model deploys to AWS EKS through the same pipeline as any release: scanned, signed, versioned, rollback-ready.
Watch it in production
Inference, inputs, and accuracy are monitored continuously, drift opens a retraining run, not a mystery ticket.
Models earn production. They don't drift into it.
Every version lives in the registry with its evaluation score. Challengers train in staging and are promoted only when they beat the champion on held-out mission data, automatically, with the evidence attached.
Tools we deliver with.
Built for the mission in front of you.
Software, data, AI, and cybersecurity built around the outcomes your team owns.
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