Forecast volatility limited planning confidence
Infrastructure teams faced rapidly shifting usage patterns that reduced trust in traditional forecasting approaches and increased operational risk in capacity planning.
We engineered forecasting systems for stability and iteration
The solution emphasized robustness, continuous retraining, and transparent model lineage to support infrastructure planning workflows under changing demand conditions.
Distributed forecasting pipelines on modern data platforms
A PySpark-based forecasting pipeline using XGBoost, MLflow, and Databricks supported scalable model training and retraining. Snowflake enabled fast access to historical and near-real-time demand signals across teams.
Improved forecast accuracy for capacity planning
More reliable forecasts stabilized planning decisions and reduced reactive capacity adjustments.
