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SQL Rules + BI Dashboards → ML Platform (Feature Store + Models)

Traditional Analytics to ML Platforms

A guide to migrating from rules-based analytics to ML-powered platforms with model training and deployment.

SQL Rules + BI Dashboards → ML Platform (Feature Store + Models) Incremental HARD Difficulty

Traditional Analytics to ML Platforms

A guide to migrating from rules-based analytics to ML-powered platforms with model training and deployment.

Estimated Timeline8-12 months
Primary Roleml-engineer

Executive Summary

A fraud detection team used 500 SQL rules (if-then) that caught only 60% of fraud. Over 10 months, they migrated to an ML platform with feature store, XGBoost models, and real-time inference, increasing fraud detection to 92% with 50% fewer false positives. This guide covers feature engineering, model development, and replacing rules with ML incrementally.

✓SQL rules become ML features (replace logic with data)
✓Feature store critical for consistency (train/serve skew)
✓Shadow mode (ML model runs alongside rules) for validation
✓Incremental replacement: start with highest-impact rules

Why Migrate from Rules-Based Analytics

The rules-based system was brittle—500 rules required 10 engineers to maintain, and fraudsters easily bypassed static rules.

  • → 60% fraud detection rate (missed 40% of fraud)
  • → 15% false positive rate (legitimate transactions declined)
  • → 10 engineers full-time on rule maintenance ($1M/year)
  • → Rules stale within weeks (fraudsters adapt)

ML Platform Readiness

The team spent 3 months on preparation: building feature store (Feast), setting up ML infrastructure, and creating label training data.

  • • Feature store (Feast, Tecton)
  • • Labeled fraud data (6 months historical)
  • • ML training infrastructure (SageMaker, Vertex AI)
  • • Real-time inference platform (Kafka + Flink)
  • • A/B testing framework

Rules-Based Assessment

The system had 500 SQL rules, 50 tables, and 100 dashboards. Rules were updated weekly based on fraud patterns.

Technical Debt

  • • Rules interdependent (changing one breaks others)
  • • No automated testing (deployments risky)
  • • Rules stale within weeks (fraudsters adapt)
  • • High false positive rate (customer complaints)

Risks

  • • ML model interpretability (explain to compliance)
  • • Training-serving skew (feature differences)
  • • Model degradation over time (concept drift)
  • • Integration with legacy rules during migration

Target ML Platform Architecture

The target was real-time fraud detection with feature store, ML models, and fallback to rules.

Feature store (Feast, 200 features)Real-time feature computation (Kafka + Flink)Model training (XGBoost, daily retraining)Model serving (KServe, 50ms latency)Rules fallback (for low-confidence predictions)

10-Month ML Platform Migration

  1. Step 1: Phase 1: Foundation (Month 1-3)

    Built feature store, labeled 1M transactions, trained baseline XGBoost model (AUC 0.85).

  2. Step 2: Phase 2: Shadow Mode (Month 4-6)

    Deployed model in shadow mode (no action), compared predictions to rules.

  3. Step 3: Phase 3: Soft Rollout (Month 7-9)

    Model actioned only for high-confidence predictions (30% of traffic).

  4. Step 4: Phase 4: Full Cutover (Month 10)

    Model for 100% of traffic, rules as fallback only.

Rules to Features Transformation

Each SQL rule was converted to a feature (input to ML model). Historical feature values computed for training.

  • • Rule → feature (e.g., transaction amount > $1000 → amount feature)
  • • Point-in-time correctness (no future data leakage)
  • • Feature backfill for historical training data
  • • Online/offline consistency (same feature logic)

Common Analytics to ML Mistakes

No feature store

Impact: Training-serving skew (model accuracy 20% lower in production)

Prevention: Feast or similar for point-in-time correct features

Skipping shadow mode

Impact: Model performs worse than rules (50% false positive increase)

Prevention: 3-month shadow mode, compare metrics

Not monitoring data drift

Impact: Model degrades silently (fraud detection 90% → 60% in 2 months)

Prevention: Evidently + daily model performance monitoring

Model too complex for real-time

Impact: Inference latency 500ms (transaction times out)

Prevention: Latency budget 50ms; use XGBoost not deep learning

Migration Success Metrics

✓Fraud detection rate: 60% → 92% (53% improvement)
✓False positive rate: 15% → 7% (53% reduction)
✓Rule maintenance cost: $1M → $100k (90% reduction)
✓Fraud losses: $5M/year → $1M/year (80% reduction)

Who Should Lead Analytics to ML Migration

Recommended Roles

Lead ML Engineer (6+ years)Data Engineer (feature store)Domain Expert (fraud analyst)MLOps Engineer (model serving)

Required Experience

  • • Feature store implementation (Feast)
  • • Real-time ML inference (50ms latency)
  • • Fraud detection domain knowledge
  • • A/B testing for ML models

Related Roles

ML Engineer Data Engineer MLOps Engineer

Frequently Asked Questions

What if ML model is less interpretable than rules?
Use SHAP for explainability. Regulators accept model explanations with SHAP values.
How to handle concept drift?
Daily retraining with new labeled data. Monitor AUC and alert on 5% drop.
Can we keep some rules alongside ML?
Yes—ensemble approach: rules for clear-cut cases, ML for complex. Start with 30% ML, increase over time.