ML Accelerators
Demand Forecasting
Predict future demand with MAPE baseline tracking
- Time series forecasting
- Seasonal pattern detection
- MAPE accuracy tracking
- Confidence intervals
Churn/Propensity Models
Identify at-risk customers and conversion opportunities
- Customer churn prediction
- Lead scoring
- Propensity to buy
- Risk segmentation
Anomaly & Fraud Detection
Detect unusual patterns and potential fraud
- Real-time anomaly detection
- Fraud pattern recognition
- Threshold alerts
- Historical comparison
LTV & Segmentation
Calculate customer lifetime value and optimize segments
- CLV prediction
- Cohort analysis
- Segment profiling
- Unit economics
MLOps Path
Data → Features
Transform raw data into ML-ready features
Model Monitoring
Drift Detection
Track data & model drift over time
- Feature drift alerts
- Prediction drift tracking
- Performance degradation
- Auto-retrain triggers
Alert Thresholds
Set performance boundaries
- Accuracy thresholds
- Precision/recall targets
- Custom metric alerts
- Stakeholder notifications
Rollback Plan
Safe model deployment strategy
- A/B testing framework
- Shadow mode deployment
- Quick rollback procedure
- Fallback to baseline
Measuring Impact
A/B Testing
Compare model vs control groups
Pre/Post Analysis
Measure before & after metrics
Uplift Calculation
Quantify improvement vs baseline
ROI Tracking
Link predictions to business value
Show uplift vs. baseline with A/B or pre/post analysis
FAQ
Q: Do we need historical data?
A: Helpful, yes; we'll scope feasibility first and determine minimum data requirements for your use case.
Q: How do you avoid model decay?
A: Drift monitors, retrain cadence, human review loops, and automated performance tracking.
Q: Can we export predictions?
A: Yes-CSV/API/BI tool connectors available for seamless integration with your workflows.
Start predicting with confidence
Request a pilot to see how our ML accelerators can deliver measurable improvements.