Time series forecasting, anomaly detection, and ML pipelines that process terabytes of data to surface actionable predictions.
ML pipelines that turn historical data into actionable foresight — at terabyte scale.
Demand planning, revenue forecasting, and capacity prediction. Models that adapt to seasonality, trends, and external shocks.
Real-time detection of fraud, equipment failures, and process deviations. Alert before the damage is done — not after.
End-to-end ML infrastructure — feature stores, training automation, model registries, and continuous monitoring in production.
Spark and Databricks pipelines that process terabytes daily. From raw data to dashboards, with ML models embedded in the flow.
Audit your data quality, volume, and freshness. Define prediction targets and success metrics before building any model.
Build feature pipelines on Spark or Databricks. Temporal features, aggregations, and domain-specific transformations that give models an edge.
Experiment with statistical, gradient-boosted, and deep learning models. Automated hyperparameter tuning and cross-validation at scale.
Drift detection, performance alerts, and automated retraining triggers. Models stay accurate as your data evolves.
Our Spark and Databricks pipelines process terabytes daily in production. We've built prediction systems for financial services giants and global consulting firms.
From feature engineering to production monitoring — we own the entire ML lifecycle. No gaps, no handoffs to separate ops teams.
We start with the business question, not the algorithm. Every model is measured by the decisions it improves, not just its accuracy score.
Tell us about your project
or email directly: fernandrez@iseeci.com