What this is
Two tracks:
- ML Discovery: problem framing, baselines, evaluation design
- ML Engineering: pipelines, deployment, monitoring, governance
Who it’s for
- Product teams with a new ML idea but unclear ROI
- Existing ML systems with unstable performance
- Teams needing reproducible pipelines (compliance, audits)
- Engineering teams integrating ML into production services
Typical engagements
ML Feasibility & Experiment Sprint (2–4 weeks)
- Define target and evaluation methodology
- Baseline models + error analysis
- Recommendation: build/no-build + roadmap
Production ML Pipeline (4–12 weeks)
- Training/evaluation/scoring pipeline design
- Automated evaluation and monitoring
- Versioning + rollback strategy
- Integration with services/data platform
Operate & Improve (monthly)
- Regression testing of model quality
- Drift investigation and remediation
- Periodic re-training decisions
Deliverables
- Experiment report (Discovery) with recommendation
- Production pipeline (Engineering) + monitoring + runbook
- Documentation: inputs/outputs, evaluation, known limitations
- Handover: “operate the model” workshop
For technical readers
- Experiment design (train/test splits, leakage checks)
- Robustness testing beyond accuracy
- Feature drift monitoring between training and scoring
- Pipeline components (preprocess/train/evaluate/score)
- Model versioning, rollback, reproducibility
- Monitoring dashboards and alerting
- Privacy/governance controls when handling sensitive data
Why I’m good at this
- Built and maintained ML pipelines (training/evaluation/scoring) using modern tooling
- Conducted experiments to answer business questions beyond accuracy
- Implemented drift monitoring and continuous evaluation
- Experience integrating ML into production workflows and communicating results
How I work
- Start with discovery to avoid building the wrong thing
- Define acceptance criteria early
- Treat ML as a product: monitoring, ownership, iteration cadence