MACHINE LEARNING — WHEN CLASSICAL BEATS GEN AI

Quick Answer: NUUN AI builds supervised, unsupervised, recommendation, and forecasting models — the classical ML that still outperforms LLMs on most structured-data problems. Production-grade builds with monitoring, interpretability, and retraining cadence. Model choice driven by problem, not hype.

WHAT WE BUILD

Classification models. Churn, fraud, propensity, quality, risk scoring.
Regression models. LTV, demand, price elasticity, volume forecasting.
Recommendation systems. Collaborative filtering, content-based, hybrid.
Forecasting. Time-series (Prophet, SARIMA, Temporal Fusion Transformer).
Clustering and anomaly detection. Customer segmentation, outlier detection, quality signals.
Computer vision. Classification, detection, and segmentation where appropriate.

HOW WE DO IT

Problem framing. What decision, what accuracy threshold, what latency.
Data and feature engineering. Domain-informed features beat AutoML for most problems.
Model selection. Gradient-boosted trees for tabular; deep learning for unstructured; per problem.
Evaluation on business metrics. Model accuracy alone doesn't run the business.
Ship with MLOps. Monitoring, drift detection, retraining, and rollback.

WHEN CLASSICAL ML BEATS GEN AI

Structured tabular data with clear labels.
High-volume real-time inference with low-latency requirements.
Interpretable scores required for regulatory or operational reasons.
Cost-sensitive inference where LLM per-call costs are prohibitive.

SELECTED WORK

Financial services client — Fraud model → false-positive rate down [X]%, fraud loss down [X]%. Read case →
E-commerce client — Recommendation system → AOV up [X]%, cross-sell attach up [X]%. Read case →

SOURCES & FURTHER READING

AI & Digital Transformation practice
Predictive analytics
Generative AI build
MLflow — https://mlflow.org/
Google Cloud AI — https://cloud.google.com/products/ai

Frequently asked.

Gradient boosting or deep learning?

For tabular problems, gradient-boosted trees (XGBoost, LightGBM, CatBoost) remain state-of-the-art for most cases. Deep learning wins on unstructured data (images, text, time-series with multiple covariates). We recommend per problem.

Do you use AutoML?

Sometimes, for baseline models or to accelerate initial iteration. Production models usually benefit from domain-driven feature engineering that AutoML can't replicate.

How do you handle model drift?

Monitoring of data distributions, model performance on actuals, and business-outcome tracking. Retraining triggered by drift detection or scheduled cadence; both have their place.

Can models be interpretable?

Yes — SHAP, LIME, and model-card documentation standard. For regulated industries (financial services, healthcare), interpretability is mandatory; for most business use cases, it's still valuable.

What platforms do you deploy on?

Cloud-native ML platforms (Vertex AI, SageMaker, Azure ML) or custom deployment on Snowflake, Databricks, or Kubernetes. Inference via APIs or batch, depending on use case.