Know Your ML Architecture Risks Before They Break at Scale
A 3-day independent review of your ML stack — models, pipelines, and infrastructure — with an architecture diagram and prioritised remediation roadmap.
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The ML Architecture Review is the fastest way to understand your ML stack’s production readiness — and the entry point for every mlai.qa engagement.
What the Review Covers
Most ML systems are built incrementally — a notebook model, a script to serve it, a cron job to retrain it. What starts as a prototype becomes production infrastructure without ever being designed as such. The Architecture Review surfaces the gaps between what you have and what you need.
Training infrastructure — Is your training pipeline reproducible? Can you retrain on demand or is it a manual process? Do you track experiments? Is your model registry a shared Google Drive folder?
Data pipeline — Where does your training data come from? How is it processed? What happens when an upstream data source changes? Is your feature engineering tightly coupled to your training code or properly abstracted?
Model serving — How does your model get from training to production? What’s the deployment process? What’s the rollback process? What’s the latency at p99? What happens when the model degrades?
Monitoring & observability — Do you monitor model performance in production? Data drift? Feature drift? Or do you find out about model failures when users complain?
Why an Independent Review
An independent architecture review finds things internal teams miss — not because internal teams aren’t capable, but because the people closest to a system have the hardest time seeing its structural problems. We’ve reviewed enough ML stacks to know which patterns fail at scale.
The executive summary deliverable provides documentation that carries weight with investors, enterprise procurement teams, and technical due diligence reviewers that internal documentation cannot.
Engagement Phases
Stack Inventory & Architecture Mapping
Structured review of your ML stack end-to-end — training infrastructure, data pipeline, model registry, serving layer, monitoring, and deployment process. We produce a current-state architecture diagram capturing every component and dependency.
Bottleneck Analysis & Risk Assessment
Deep analysis of the architecture against production requirements — scalability, reliability, maintainability, and cost. We identify bottlenecks, single points of failure, and architectural debt that will compound as you scale.
Architecture Report & Recommendations
Delivery of the Architecture Review Report: current-state diagram, findings ranked by severity and urgency, recommended target-state architecture, and a prioritised remediation roadmap. Includes a 30-minute debrief call.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Architecture Visibility | No documented ML architecture — decisions scattered across Slack and notebooks | Complete current-state diagram and decision log delivered in 72 hours |
| Investor Readiness | No independent ML architecture assessment for Series B data room | Executive summary and architecture review report suitable for investor due diligence |
| Risk Reduction | Unknown architectural risks that could cause production incidents or rewrites | Top 5 risks identified and prioritised — remediation roadmap ready to execute |
Tools We Use
Frequently Asked Questions
What access do you need to run the review?
We work from documentation, architecture diagrams, and a structured intake questionnaire — we do not require production system access. For teams comfortable sharing more, we can review infrastructure code, pipeline configurations, and monitoring dashboards directly. Most teams complete the intake in under 2 hours, async.
What if we don't have any architecture documentation?
That's common — and exactly why the review is valuable. We conduct a structured interview with your technical lead and work from code repositories, deployment configs, and any existing diagrams. The output of the review is often the first formal documentation of your ML architecture.
How is this different from a generic cloud architecture review?
We focus exclusively on the ML stack — training infrastructure, data pipelines, feature engineering, model serving, and ML-specific monitoring (drift, data quality, model performance). Cloud infrastructure reviews miss the ML-specific patterns that cause most AI system failures.
What happens after the review?
You receive the Architecture Review Report with a remediation roadmap. You choose what to act on — there is no obligation. For teams that proceed, the review fee is credited against the first sprint engagement. Most clients use the remediation roadmap to scope their next 90 days of ML infrastructure work.
Build ML that scales.
Book a free 30-minute ML architecture scope call with our experts. We review your stack and tell you exactly what to fix before it breaks at scale.
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