ML & Forecasting Systems
Models that run in production, not just in notebooks.
I build prediction systems that close the gap between research and production.
Who this is for
- → Supply chain and logistics companies needing demand forecasting
- → E-commerce platforms optimizing inventory and pricing
- → Financial firms building predictive risk models
- → Startups productionizing their first machine learning systems
Problems this solves
- • Research models can't handle production data volumes or latency requirements
- • No reliable pipeline from raw data to deployed predictions
- • Models degrade over time with no monitoring or retraining logic
- • Business outcomes disconnected from model metrics
What I do
Demand & Inventory Forecasting
Time-series modeling for fulfillment, replenishment, and stockout avoidance
Classification Systems
Product classification, anomaly detection, risk scoring at scale
Feature Engineering
Large-scale pipeline design for high-dimensional, noisy real-world data
Production ML
TFX, Kubeflow, Dataflow pipelines with monitoring and alerting
Model Evaluation
Business-aligned metrics, A/B testing, drift detection
Deliverables
- ✓ Trained and validated model with documented performance
- ✓ Production-ready inference pipeline
- ✓ Monitoring and alerting setup
- ✓ Technical handoff documentation
Example outcomes
7 production forecasting models at Blue Yonder across supply-chain verticals
5TB+ data pipeline processing at scale with Apache Beam and Dataflow
50% manual effort reduction through ML-driven automation at GoGlocal
Tools & methods
Related case studies
Lets collaborate!
Whether you need a quantitative researcher, an machine learning systems builder, or a technical advisor — I'm available for select consulting engagements.
Get in Touch →