Machine Learning

Machine learning is only valuable when it solves a real problem with measurable impact. Sekoya helps organizations move beyond proof-of-concept notebooks and into production-grade ML systems that deliver reliable predictions, automate repetitive decisions, and surface insights hidden in large datasets.

Our ML engineers work alongside domain experts to frame the right problem before writing a single line of model code. We handle the full pipeline: data exploration and feature engineering, model selection and hyperparameter tuning, rigorous evaluation against business metrics, and deployment into scalable serving infrastructure. We work with frameworks including TensorFlow, PyTorch, and scikit-learn, and deploy on platforms ranging from cloud-managed endpoints to on-premise GPU clusters.

Equally important is what happens after launch. Models degrade as the world changes, so we build MLOps pipelines that monitor prediction quality, detect data drift, and trigger automated retraining when performance drops below acceptable thresholds. The result is an ML system that stays accurate over time — not a one-off experiment that decays in production.