Your evals chose the wrong model
Model A beats Model B on benchmarks, but performs worse in production. Offline metrics don't capture real-world failure modes or distribution shifts.
Fix ML Systems That Break at Scale
System-level ML audits by a Principal Machine Learning Engineer. I focus on model choice, evaluation, and inference decisions—and how they interact with deployment—to cut latency, reduce costs, and stabilize production behavior. No fluff. Just results.
Book a 30-Minute ML Systems AuditPrincipal ML Engineer · Former NSF-funded researcher · Production AI at scale
When You Should Talk to Me
If your ML system behaves strangely, costs too much, or falls apart at scale—I've probably seen why. Here are patterns I diagnose regularly:
Latency is dominated by what you didn't measure
The model is fast, but end-to-end latency is terrible. Preprocessing, tokenization, or serialization overhead destroys performance.
Fine-tuning improved metrics but broke behavior
Your fine-tuned model scores better but users complain. Objective mismatch or overfitting masked by evaluation blind spots.
RAG works in demos, fails in production
Retrieval quality degrades at scale. Context windows overflow silently. Latency balloons with corpus size.
Prompt drift causes silent regressions
Nothing changed in code, but outputs degraded. Unversioned prompts and templates create non-reproducible behavior.
Costs exploded after deployment
What worked in development becomes financially unsustainable in production. No one modeled the real cost structure.
Services
ML Systems Audit
30 minutes
Rapid system-level diagnosis of your ML pipeline. Live session identifying bottlenecks, risks, and immediate wins.
- Live diagnosis
- Immediate fixes
- Clear next steps
Deep ML Systems Diagnostic
1-2 weeks
Comprehensive analysis of your ML system: evals, model choices, inference paths, deployment, and observability.
- System architecture map
- Bottleneck analysis
- Prioritized interventions
- Risk assessment
- 10-15 page report
Targeted Intervention
2-4 weeks
Fix a specific critical issue. Design the solution, review implementation, unblock hard problems.
- Solution design
- Implementation guidance
- Code reviews
- Performance validation
About Davix Labs
Davix Labs is led by Marcel Bischoff, a Principal Machine Learning Engineer with a background in mathematical physics and NSF-funded research. Marcel focuses on real-world AI systems in production—diagnosing ML decisions that break at scale and optimizing for performance, reliability, and cost.
Get Started
Ready to diagnose what's breaking in your ML system?
Schedule a 30-Minute AuditOr email: marcel@localconformal.net