Impact & Outcomes
This page highlights measurable outcomes from production ML systems, GenAI platforms, and MLOps initiatives I’ve led or contributed to. Numbers are sanitized to respect confidentiality while demonstrating scale and impact.
Production ML & Platform Impact
1. ML Platform Rollout Safety Nets
Challenge: Model deployments lacked rollback infrastructure. Bad models reached production, causing customer-facing issues before teams could react.
Solution: Designed and implemented control planes for model rollout with shadow deployment validation, golden test suites for regression detection, and automated traffic failover runbooks.
Impact:
- 100% reduction in customer-impacting model regressions after rollback infrastructure deployment
- < 5 min mean time to rollback (MTTR) for model issues vs. previous multi-hour manual interventions
- 3 major incidents prevented in first 6 months via shadow deployment detection
See case study: Platform Guardrails for ML Services
2. Ads Incrementality Measurement at Scale
Challenge: Attribution models showed correlation but couldn’t answer causal questions. Stakeholders needed to know: “Do our ads actually drive incremental purchases?”
Solution: Built causal measurement framework using geo-based experiments, synthetic controls, and CUPED variance reduction. Designed executive dashboards communicating lift estimates with confidence intervals.
Impact:
- Lift measurement infrastructure serving 10M+ daily ad impressions with counterfactual simulations
- 15-20% measured incrementality on key campaigns (actual lift vs. baseline), enabling data-driven budget allocation
- 40% reduction in experiment runtime via variance reduction techniques while maintaining statistical power
See case study: Ads Incrementality at Scale
3. GenAI Safety Rails & Evaluation Loops
Challenge: LLM-based copilots for product Q&A and creative generation lacked structured evaluation. Teams relied on manual spot-checks and “vibe-based” quality assessment.
Solution: Implemented evaluation loops with LLM-as-judge patterns, rule-based safety checks, and human feedback integration. Built CI/CD pipelines that gate deployments on eval metrics.
Impact:
- Safety incident rate reduced by >90% (hallucinations, off-brand responses) post-eval loop deployment
- Evaluation coverage increased to 100% of production traffic via automated evals (vs. <5% manual spot-check baseline)
- 2-day deployment cycle for GenAI feature iterations (down from 2+ weeks manual validation)
4. CI/CD/CT for ML: Reducing Model Deployment Friction
Challenge: ML teams lacked standardized deployment pipelines. Each project reimplemented training, validation, and serving infrastructure, causing delays and inconsistency.
Solution: Designed and evangelized CI/CD/CT (Continuous Training) templates with schema-based contracts, automated golden tests, and observability-by-default patterns. Delivered workshops and reference implementations.
Impact:
- 60% reduction in time-to-production for new ML models across 15+ teams
- Zero schema-related production incidents after contract enforcement adoption
- 30+ teams adopted standardized ML deployment templates within 12 months
5. GNN-Based Recommendations Serving Optimization
Challenge: Graph neural network models for product recommendations had serving latency >500ms, exceeding product requirements for real-time personalization.
Solution: Re-architected GNN serving with batch size tuning, cached embeddings for hot items, and async inference patterns. Implemented latency budgeting and SLO monitoring.
Impact:
- Latency reduced from >500ms to <100ms p99 serving latency (5x improvement)
- 10x throughput increase enabling real-time personalization for high-traffic product pages
- Cost efficiency: Same infrastructure served 10x more QPS post-optimization
Get in Touch
Interested in:
- Hiring: See my CV and case studies
- Speaking: Visit the speaking page for talk proposals and media kit
- Collaboration: Contact me to discuss ML architecture, causal measurement, or platform initiatives
Methodologies & Principles
The outcomes above follow consistent patterns:
1. Contracts over Cowboy Code Schema-based contracts between data, models, and services prevent silent failures and enable safe iteration.
2. Observability by Default If it’s not instrumented, it’s not production-ready. Metrics, logs, and traces from day one.
3. Rollback First, Deploy Second If you can’t roll it back safely, don’t deploy it. Control planes and kill switches are table stakes.
4. Measure What Matters Correlation is easy. Causation is hard. Invest in causal measurement and communicate uncertainty honestly.
5. Platform Thinking Build systems that make the right thing easy and the wrong thing hard. Force functions > documentation.
Want to see how these principles apply to your systems? Get in touch.
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