Machine learning for federal missions.

What we deliver

Most federal ML work stalls in the same place: a promising notebook on a laptop that never becomes a production system. We close that gap. Every engagement is scoped toward a working model serving real users, not a PowerPoint deck.

• Computer vision — object detection, segmentation, OCR, document understanding, satellite/aerial imagery analysis, medical imaging.
• Natural language processing — classification, named entity recognition, summarization, embeddings-based search, fine-tuned domain models.
• Time-series forecasting — demand, supply, workforce, equipment, budget. Classical (Prophet, ARIMA) through deep (N-BEATS, TFT).
• Anomaly detection — fraud, network intrusion, supply chain deviation, health surveillance, predictive maintenance.
• Tabular prediction — gradient-boosted trees (XGBoost, LightGBM, CatBoost) plus ensembling. The unglamorous workhorse that wins most federal problems.
• MLOps — model registry, drift monitoring, shadow deployment, A/B testing, rollback paths.

Why Kaggle matters for federal ML

A Kaggle Top 200 ranking means you've consistently beaten 199,800+ other data scientists on held-out test sets across diverse problem types. It is a direct publicly-benchmarked, adversarial test of modeling skill that exists.

For federal work, it matters because most federal ML projects fail not from lack of data but from modeling choices — wrong architecture, leaked features, overfit validation, no uncertainty quantification. Competition ML training directly addresses these failure modes.

Stack

• Modeling: PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, HuggingFace Transformers, timm, detectron2.
• Experiment tracking: MLflow, Weights & Biases.
• Serving: TorchServe, ONNX Runtime, Triton Inference Server, FastAPI wrappers.
• Feature stores & data: Feast, Parquet + DuckDB, Postgres, Spark for batch feature engineering.
• Cloud: AWS GovCloud (SageMaker, EC2 GPU), Azure Government, on-prem CUDA clusters.

Past performance highlights