Identify data sources, target KPIs and forecast horizons in a 90-minute session.
Clean and load data into Amazon Forecast, compare algorithms and validate accuracy on holdout sets.
Automate daily pipelines with Lambda and Step Functions, expose results via API and dashboards.
Retrain models, add new signals and roll out use cases like anomaly detection or price optimization.
COO, FreshMart
Average 94 percent forecast accuracy across pilot projects
30 percent reduction in working capital tied up in inventory
Prototype churn model lacked accuracy, scale, and explainability—studio owners had no early warning when members were likely to cancel.
Avahi rebuilt the platform on Amazon SageMaker, replacing the decision tree with a tuned XGBoost model and an automated MLOps pipeline that delivers real-time predictions and LIME explanations.
0.846 AUC validation accuracy on the new model
Real-time predictions with member-level churn drivers surfaced via LIME
Automated monthly retraining (or on performance drop) keeps models fresh
End-to-end SageMaker workflow lets studios act proactively to reduce churn
More is better, but many SMBs see strong results with 12 to 24 months of clean data.
Yes. We merge any relevant signals to improve accuracy.
You pay for data ingestion, training time and the number of forecasts generated. Most small and midsize clients spend less than traditional BI tools.
