Chapter 6.3: Investment Prioritization

“The essence of strategy is choosing what not to do.” — Michael Porter

You can’t build everything at once. This chapter provides frameworks for prioritizing MLOps investments to maximize early value and build momentum for the full platform.

6.3.1. The Sequencing Paradox

Every MLOps component seems essential:

• “We need a Feature Store first—that’s where the data lives.”
• “No, we need Monitoring first—we’re flying blind.”
• “Actually, we need CI/CD first—deployment is our bottleneck.”

The reality: You need all of them. But you can only build one at a time.

The Goal of Prioritization

1. Maximize early value: Deliver ROI within 90 days.
2. Build momentum: Early wins fund later phases.
3. Reduce risk: Prove capability before large commitments.
4. Learn: Each phase informs the next.

6.3.2. The Value vs. Effort Matrix

The classic 2x2 prioritization framework, adapted for MLOps.

The Matrix

MLOps Components Mapped

Scoring Methodology

Value Score (1-5):

• 5: Directly reduces costs or increases revenue by >$1M/year
• 4: Significant productivity gain (>30%) or risk reduction
• 3: Moderate improvement, visible to stakeholders
• 2: Incremental improvement
• 1: Nice to have

Effort Score (1-5):

• 5: >6 months, multiple teams, significant investment
• 4: 3-6 months, cross-functional
• 3: 1-3 months, dedicated team
• 2: Weeks, single team
• 1: Days, single person

6.3.3. Dependency Analysis

Some components depend on others. Build foundations first.

MLOps Dependency Graph

flowchart TD
    A[Data Infrastructure] --> B[Feature Store]
    A --> C[Experiment Tracking]
    C --> D[Model Registry]
    B --> E[Training Pipelines]
    D --> E
    E --> F[CI/CD for Models]
    D --> G[Model Serving]
    F --> G
    G --> H[Monitoring]
    H --> I[Automated Retraining]
    I --> E

Dependency Matrix

Reading the Matrix

• Don’t start Serving without Registry: You need somewhere to pull models from.
• Don’t start Retraining without Monitoring: You need to know when to retrain.
• Tracking and Registry can be early wins: Minimal dependencies, high visibility.

6.3.4. The Quick-Win Strategy

Show value in 30-60-90 days.

Days 0-30: Foundation + First Win

Value Delivered: Reproducibility, visibility, first audit trail.

Days 30-60: First Production Model

Value Delivered: First model deployed via MLOps pipeline.

Days 60-90: Scale and Automate

Value Delivered: Multiple models on platform, measurable productivity gains.

6.3.5. The ROI-Ordered Roadmap

Sequence investments by payback period.

Typical MLOps ROI by Component

Optimal Sequence (Balancing ROI and Dependencies)

1. Basic Monitoring: Fastest payback, immediate visibility.
2. Experiment Tracking + Model Registry: Foundation, fast wins.
3. CI/CD for Models: Unlocks velocity.
4. Feature Store: Highest absolute value.
5. Automated Training: Unlocks continuous improvement.
6. A/B Testing: Enables rigorous optimization.
7. Advanced Serving: Performance at scale.

6.3.6. Pilot Selection

Choosing the right first model matters.

Pilot Selection Criteria

Good vs. Bad Pilot Choices

Pilot Agreement Template

MLOps Pilot Agreement

Pilot Model: [Name]
Sprint: [Start Date] to [End Date]

Success Criteria:
- [ ] Model deployed via new pipeline
- [ ] Deployment time reduced by >50%
- [ ] Model monitoring active
- [ ] Team satisfaction >4/5

Team Commitments:
- Data Science: [Name] - 50% time allocation
- Platform: [Name] - Full-time support
- DevOps: [Name] - On-call support

Decision Point:
At [Date], evaluate success criteria and decide on Phase 2.

6.3.7. Phase Gate Approach

Structure investment to reduce risk.

Phase Gates for MLOps

Phase Gate Review Template

Phase 1 Gate Review

Metrics Achieved:
- Deployment time: 6 weeks → 3 days ✅
- Model uptime: 99.5% ✅
- Team satisfaction: 4.2/5 ✅
- Budget: 95% of plan ✅

Lessons Learned:
- Feature Store integration took longer than expected
- DevOps onboarding needs more attention

Risks for Phase 2:
- Data engineering capacity constrained
- Mitigation: Add 1 contractor

Recommendation: PROCEED to Phase 2
Investment Required: $600K
Timeline: Q2-Q3

6.3.8. Budget Allocation Models

How to structure the investment.

Model 1: Phased Investment

Pros: High initial investment shows commitment. Cons: Large upfront ask.

Model 2: Incremental Investment

Pros: Lower initial ask, prove value first. Cons: Slower to full capability.

Model 3: Value-Based Investment

Tie investment to demonstrated value:

• Release $500K after ROI of $1M proven.
• Release $1M after ROI of $3M proven.

Pros: Aligns investment with outcomes. Cons: Requires good metrics from day 1.

6.3.9. Roadmap Communication

Different stakeholders need different views.

Executive Roadmap (Quarterly)

┌─────────┬─────────┬─────────┬─────────┬─────────┐
│   Q1    │   Q2    │   Q3    │   Q4    │  Y2+    │
├─────────┼─────────┼─────────┼─────────┼─────────┤
│Foundation│ Pilot  │ Scale   │ Full    │ Optimize │
│ $200K  │ $400K   │ $600K   │ $400K   │ $200K/Q │
│ 2 models│ 5 models│ 15 models│ All     │         │
└─────────┴─────────┴─────────┴─────────┴─────────┘

Technical Roadmap (Monthly)

User Roadmap (What Changes for Me)

6.3.10. Key Takeaways

1. You can’t do everything at once: Sequence matters.

2. Start with quick wins: Build credibility in 30-60 days.

3. Follow dependencies: Registry before Serving, Monitoring before Retraining.

4. Use phase gates: Commit incrementally, prove value, earn more investment.

5. Pick the right pilot: High visibility, moderate complexity, motivated team.

6. Communicate the roadmap: Different views for different stakeholders.

7. Tie investment to value: Show ROI, get more budget.

Next: 6.4 Common Objections & Responses — Handling resistance to MLOps investment.