We deliver machine learning development services for production use cases — classification, regression, forecasting, recommendation, ranking — with proper feature engineering, model versioning, and MLOps pipelines.
A model that works on a held-out test set can fail in production for half a dozen reasons — data drift, feature skew, training-serving mismatch, scale, latency, monitoring blind spots. Production ML is operations work as much as it is modelling work.
Feature store as the single source of truth for features in training and serving. Model versions with full lineage. Training pipelines reproducible in CI. Drift and skew monitoring in production. Rollback strategy when reality deviates from training.
Production models with full lineage, drift monitoring, A/B-tested releases, and a documented retraining cadence. The team can change one feature without rebuilding the system.
Tabular ML at scale — XGBoost, LightGBM, deep tabular models — with feature engineering and proper validation.
Forecasting at fleet scale — Prophet, statsforecast, neural forecasting models, hierarchical reconciliation.
Collaborative filtering, two-tower models, sequence models — with cold-start and exploration strategies.
Feast or in-house feature stores — single source of truth for training and serving features.
Training pipelines, model registries, deployment infrastructure, and monitoring on MLflow / Kubeflow / SageMaker.
Production drift detection, A/B harnesses for model releases, and retraining triggers tied to drift signals.