Production Machine Learning & Data Infrastructure
Models and pipelines that run in production - not just in notebooks.
I turn Machine Learning prototypes into reliable production systems, and restructure fragmented data infrastructure so your team can trust the numbers and act on them.
Who this is for
- → Startups with a working Machine Learning prototype that needs to become a real, scalable production system
- → Mid-size firms with fragmented data infrastructure that needs an audit, restructure, and automation
- → Teams shipping forecasting, classification, or risk models that have to hold up at scale
Problems this solves
- • A model that scores well in a notebook but can't meet production latency, volume, or reliability
- • Fragile, undocumented pipelines that break with any upstream change
- • Models that silently degrade with no monitoring or retraining
- • Manual data work consuming time that should go to insight
What I do
Forecasting & Classification at Scale
Time-series, demand and inventory, anomaly detection, and risk scoring on real-world data volumes
Leakage-Safe Feature Engineering
Pipelines on high-dimensional, noisy data - where most models actually fail, not in model selection
Production Machine Learning
TFX / Apache Beam / Dataflow pipelines with monitoring, alerting, and retraining logic
Data Infrastructure
ETL design, multi-source integration, audit trails, and operational dashboards
Deliverables
- ✓ A validated model with documented performance and failure modes
- ✓ A production inference or data pipeline with monitoring and alerting
- ✓ A measurable reduction in manual effort on the workflow I own
- ✓ Documentation and a handoff so your team owns it after I leave
How we'd work together
Diagnostic review (1–2 weeks)
Audit of your data infrastructure or Machine Learning system - correctness, scalability, cost, and a prioritized roadmap.
Build sprint (4–8 weeks)
Productionize a model or restructure a pipeline with monitoring and tests.
Full engagement (2–6 months)
End-to-end build from problem framing to a deployed, monitored, documented system.
Example outcomes
7 production forecasting models across supply-chain verticals at Blue Yonder, on 5TB+ data
ETL across 70+ source systems for APRA regulatory compliance at ANZ Bank
1,000+ SKUs automated across Amazon, eBay, Walmart, and Lazada at GoGlocal - 50% less manual effort
Tools & methods
Related case studies
Have a problem worth solving?
Whether you need a quantitative researcher, a Machine Learning systems builder, or a technical advisor, I take a small number of consulting engagements at a time.
Book a call →