Example: Build vs Buy Decision (Single-Round)
User Query: "Should we build internal ML infrastructure or use vendor solutions?"
Expert Panel: ML Infrastructure Strategy
Panel Members:
- Dr. Sarah Chen, Software Architect
- James Morrison, McKinsey Consultant
- David Kumar, Finance Analyst
- Alexandra Torres, DevOps Engineer
Dr. Sarah Chen, Software Architect
The technical decision hinges on three factors: customization needs, team capability, and long-term flexibility.
Build Considerations:
- Full control over model architecture and feature engineering
- Can optimize for MSD's specific assay data characteristics
- Integration with existing Python data pipelines (pandas, scikit-learn already in use)
- Team has Python/AWS expertise but limited ML ops experience
Buy Considerations:
- AWS SageMaker provides managed infrastructure we already use
- Reduces time-to-production by 6-9 months
- Built-in monitoring, versioning, A/B testing capabilities
- Risk: vendor lock-in, less control over model explainability (critical for FDA submissions)
Hybrid Recommendation: Start with SageMaker for rapid prototyping, build custom components only where regulatory explainability demands it.
James Morrison, McKinsey Consultant
Let's structure this with a classic make-vs-buy framework, examining strategic fit and capability gaps.
Issue Tree:
ML Infrastructure Decision
├─ Strategic Importance
│ ├─ Core competency? (ECL assays = yes, ML infrastructure = no)
│ ├─ Competitive advantage source? (Unlikely—models matter, infra doesn't)
│ └─ Regulatory moat? (Possibly—explainability requirements)
├─ Capability Assessment
│ ├─ Current: Strong software eng, weak ML ops
│ ├─ Gap: Model deployment, monitoring, drift detection
│ └─ Build time: 12-18 months to production-grade
└─ Economics
├─ Build: High capex, low opex
└─ Buy: Low capex, high opex (scales with usage)MECE Analysis: The only defensible "build" argument is regulatory explainability requirements. Everything else favors vendor solutions.
Recommendation: Buy for infrastructure, build for domain-specific model components. This is a classic "partner on non-core capabilities" scenario.
David Kumar, Finance Analyst
5-Year TCO Comparison:
Build Option:
- Year 1: $800K (3 ML engineers, AWS infrastructure)
- Years 2-5: $450K/year (maintenance, scaling)
- Total: $2.6M
- Risk: High upfront investment, 12-18 month delay to revenue
Buy Option (SageMaker):
- Year 1: $120K (training, inference, storage)
- Years 2-5: $180K/year (scales with model usage)
- Total: $840K
- Risk: Vendor dependency, potential price increases
Break-Even Analysis: Build becomes cost-competitive only if we deploy 50+ production models. Current roadmap shows 8-12 models over 3 years.
IRR Impact: Delayed launch (build scenario) costs $1.2M in opportunity cost (6-month market delay × $200K monthly revenue).
Recommendation: Buy for Phase 1 (3 years), revisit if model portfolio exceeds 30 models.
Alexandra Torres, DevOps Engineer
Operational Reality Check:
Current Infrastructure:
- AWS ECS for containers, Jenkins for CI/CD
- No existing ML ops tooling (no MLflow, no model registry)
- On-call team of 4, already at capacity for production incidents
Build Operational Cost:
- New on-call rotation for ML infrastructure (+1.5 FTE)
- Model monitoring and drift detection systems (custom build = 6 months)
- Integration with existing observability stack (DataDog)
- Training required: team has limited Kubernetes/Kubeflow experience
Buy Operational Benefits:
- SageMaker integrates with existing AWS IAM, CloudWatch, ECS
- Managed endpoints eliminate infra on-call burden
- Built-in model registry, versioning, rollback
The DevOps Concern: Building means we own incident response for ML infra during regulatory audits. That's high-stakes operational risk with limited team depth.
Recommendation: Strong bias toward vendor solution to avoid operational overstretch.
Synthesis (RAPID Framework)
Recommend: Start with AWS SageMaker for ML infrastructure, build custom explainability layer for regulatory compliance.
Rationale:
- Strategic: ML infrastructure is not core competency (Chen, Morrison)
- Financial: 3x cost advantage, faster time-to-revenue (Kumar)
- Operational: Reduces operational burden on constrained team (Torres)
- Risk Mitigation: Hybrid approach preserves regulatory control (Chen)
Agree: Engineering leadership, Finance, Quality/Regulatory
Perform:
- Platform team: SageMaker setup and integration
- ML team: Model development and explainability layer
- DevOps: Monitoring and deployment automation
Input:
- Regulatory: Explainability requirements review
- Customer Success: Model performance SLAs
- Security: Vendor risk assessment
Decide: VP Engineering (with CFO sign-off for budget)
Implementation Roadmap:
- Month 1-2: SageMaker pilot with one non-critical model
- Month 3-4: Build custom explainability wrapper for regulatory-critical models
- Month 5-6: Production deployment with monitoring
- Quarter 2: Evaluate model portfolio growth for build vs continue-buy decision