AI Technical Debt Assessment Template: Identify and Prioritize ML System Debt

AI systems accumulate technical debt faster than traditional software. From stale training data and deprecated model versions to brittle pipelines and undocumented feature engineering, ML technical debt compounds silently until it becomes a crisis. This template helps you systematically assess, score, and prioritize AI technical debt before it slows your team to a crawl.

Why AI Technical Debt Is Different

The 7 Types of AI Technical Debt

1. Data Debt

Stale datasets, undocumented transformations, missing validation

2. Model Debt

Outdated architectures, no versioning, unexplained predictions

3. Pipeline Debt

Fragile ETL, manual steps, no reproducibility

4. Monitoring Debt

No drift detection, missing alerts, blind spots in production

5. Testing Debt

No evaluation suites, untested edge cases, no regression tests

6. Documentation Debt

Tribal knowledge, no model cards, missing decision logs

7. Infrastructure Debt

Over-provisioned GPUs, no auto-scaling, vendor lock-in

Why It Compounds

Each type feeds the others, creating cascading failures over time

AI Technical Debt Assessment Template

Copy and customize this template for your AI system audits:

╔══════════════════════════════════════════════════════════════════╗ ║ AI TECHNICAL DEBT ASSESSMENT ║ ╠══════════════════════════════════════════════════════════════════╣ ASSESSMENT OVERVIEW ──────────────────────────────────────────────────────────────────── System Name: [AI system or product name] Assessment Lead: [PM / Tech Lead name] Assessment Date: [YYYY-MM-DD] Last Assessment: [YYYY-MM-DD or "First assessment"] Team: [ML Engineering, Data, Product] Scope: [Specific components being assessed] SYSTEM HEALTH SNAPSHOT ──────────────────────────────────────────────────────────────────── Overall Debt Score: [X/100] (higher = more debt) Risk Level: [Low / Medium / High / Critical] Trend: [Improving / Stable / Worsening] Sprint Capacity Lost:[X%] (time spent on debt-related issues) ╠══════════════════════════════════════════════════════════════════╣ ║ DEBT CATEGORY SCORING ║ ╠══════════════════════════════════════════════════════════════════╣ Score each item: 1 (minimal debt) to 5 (critical debt) 1. DATA DEBT ──────────────────────────────────────────────────────────────────── Data freshness (how old is training data?) [1-5] Data documentation (schemas, lineage, cards) [1-5] Data validation (quality checks in pipeline) [1-5] Data versioning (can you reproduce datasets?) [1-5] Feature store (centralized, reusable features) [1-5] ──────────────────────────────────────────────────────────────────── Data Debt Score: [X/25] Notes: [Specific data debt observations] 2. MODEL DEBT ──────────────────────────────────────────────────────────────────── Model versioning (tracked, reproducible) [1-5] Model staleness (when last retrained) [1-5] Model explainability (can you explain outputs) [1-5] Model complexity (simpler alternatives exist?) [1-5] Model dependencies (chained model risks) [1-5] ──────────────────────────────────────────────────────────────────── Model Debt Score: [X/25] Notes: [Specific model debt observations] 3. PIPELINE DEBT ──────────────────────────────────────────────────────────────────── Pipeline automation (manual steps remaining?) [1-5] Pipeline reliability (failure rate, recovery) [1-5] Pipeline reproducibility (can recreate runs?) [1-5] Pipeline speed (training and inference times) [1-5] Pipeline testing (CI/CD for ML pipelines) [1-5] ──────────────────────────────────────────────────────────────────── Pipeline Debt Score: [X/25] Notes: [Specific pipeline debt observations] 4. MONITORING & OBSERVABILITY DEBT ──────────────────────────────────────────────────────────────────── Drift detection (data and concept drift) [1-5] Performance monitoring (latency, throughput) [1-5] Quality monitoring (accuracy over time) [1-5] Alerting (actionable, not noisy) [1-5] Logging (inputs, outputs, decisions tracked) [1-5] ──────────────────────────────────────────────────────────────────── Monitoring Debt Score: [X/25] Notes: [Specific monitoring debt observations] 5. TESTING DEBT ──────────────────────────────────────────────────────────────────── Evaluation datasets (comprehensive, current?) [1-5] Edge case coverage (known failure modes?) [1-5] Regression testing (automated before deploy?) [1-5] A/B testing infra (can test safely in prod?) [1-5] Bias/fairness testing (regular audits?) [1-5] ──────────────────────────────────────────────────────────────────── Testing Debt Score: [X/25] Notes: [Specific testing debt observations] 6. DOCUMENTATION DEBT ──────────────────────────────────────────────────────────────────── Model cards (documented for each model?) [1-5] Decision logs (why choices were made?) [1-5] Runbooks (how to operate the system?) [1-5] API documentation (clear, up-to-date?) [1-5] Onboarding docs (new team member readiness?) [1-5] ──────────────────────────────────────────────────────────────────── Documentation Debt Score: [X/25] Notes: [Specific documentation debt observations] 7. INFRASTRUCTURE DEBT ──────────────────────────────────────────────────────────────────── Resource efficiency (right-sized compute?) [1-5] Auto-scaling (handles load dynamically?) [1-5] Cost optimization (waste minimized?) [1-5] Vendor independence (portable, no lock-in?) [1-5] Security posture (access controls, secrets) [1-5] ──────────────────────────────────────────────────────────────────── Infrastructure Debt Score: [X/25] Notes: [Specific infrastructure debt observations] ╠══════════════════════════════════════════════════════════════════╣ ║ OVERALL SCORING SUMMARY ║ ╠══════════════════════════════════════════════════════════════════╣ Category Score Max % ──────────────────────────────────────────────────────────────────── Data Debt [X] 25 [X%] Model Debt [X] 25 [X%] Pipeline Debt [X] 25 [X%] Monitoring Debt [X] 25 [X%] Testing Debt [X] 25 [X%] Documentation Debt [X] 25 [X%] Infrastructure Debt [X] 25 [X%] ──────────────────────────────────────────────────────────────────── TOTAL [X] 175 [X%] Interpretation: 0-35 (0-20%) = Healthy - maintain current practices 36-70 (21-40%) = Manageable - address in normal sprints 71-105 (41-60%) = Concerning - dedicate 20% capacity 106-140 (61-80%)= Critical - dedicated debt sprint needed 141-175 (81-100%)= Emergency - stop features, fix debt ╚══════════════════════════════════════════════════════════════════╝

Debt Prioritization Matrix

Impact vs. Effort Framework

Plot each debt item on this matrix to decide what to tackle first:

Q1: High Impact, Low Effort

Action: Fix immediately (quick wins)

Add missing data validation
Set up basic monitoring alerts
Document critical runbooks

Q2: High Impact, High Effort

Action: Plan dedicated sprints

Rebuild training pipeline
Implement model versioning
Build comprehensive eval suite

Q3: Low Impact, Low Effort

Action: Include in regular sprints

Update API documentation
Clean up unused feature flags
Standardize naming conventions

Q4: Low Impact, High Effort

Action: Defer or eliminate

Full infrastructure migration
Rewrite legacy feature engineering
Switch ML frameworks entirely

Remediation Plan Template

DEBT REMEDIATION PLAN ──────────────────────────────────────────────────────────────────── SPRINT 1 (Weeks 1-2): Quick Wins ──────────────────────────────────────────────────────────────────── Item: [Debt item description] Category: [Data / Model / Pipeline / etc.] Impact: [High / Medium / Low] Effort: [S / M / L] Owner: [Team member name] Definition of Done: [Specific completion criteria] Status: [ ] Not Started [ ] In Progress [ ] Done Item: [Debt item description] Category: [Data / Model / Pipeline / etc.] Impact: [High / Medium / Low] Effort: [S / M / L] Owner: [Team member name] Definition of Done: [Specific completion criteria] Status: [ ] Not Started [ ] In Progress [ ] Done SPRINT 2 (Weeks 3-4): High-Impact Items ──────────────────────────────────────────────────────────────────── [Repeat item template above] SPRINT 3 (Weeks 5-6): Systematic Improvements ──────────────────────────────────────────────────────────────────── [Repeat item template above] ONGOING PRACTICES ──────────────────────────────────────────────────────────────────── [ ] Reserve 20% sprint capacity for debt reduction [ ] Run quarterly debt assessments using this template [ ] Track debt score trend over time (target: decreasing) [ ] Include debt items in sprint retrospectives [ ] Celebrate debt reduction milestones

Common AI Debt Patterns to Watch

Top 5 Debt Traps in AI Systems

1. The "It Works in Jupyter" Trap

Models developed in notebooks without proper productionization. Code that works locally but has hidden dependencies, hardcoded paths, and no error handling.

Fix: Enforce a notebook-to-production pipeline with code review gates.

2. The "Nobody Knows Why" Model

A model in production that works but nobody on the current team understands how it was trained, what data was used, or why certain architectural choices were made.

Fix: Mandate model cards and decision logs for every production model.

3. The "Training Data Time Bomb"

Training data that was appropriate at launch but has drifted significantly from production reality. Performance degrades slowly until a sudden cliff.

Fix: Implement automated data freshness checks and drift detection alerts.

4. The "Golden Pipeline" Problem

A single, fragile pipeline that everyone is afraid to touch. No tests, no documentation, and one person who "knows how it works."

Fix: Document first, add tests second, then refactor incrementally.

5. The "GPU Graveyard"

Over-provisioned compute resources running 24/7 for models that are only queried during business hours, wasting thousands in cloud costs monthly.

Fix: Implement auto-scaling and regular cost audits of compute resources.

Recommended Assessment Cadence

Monthly (Lightweight)

Review monitoring dashboards
Check data freshness scores
Update debt backlog items
15-minute team check-in

Quarterly (Full Assessment)

Complete this full template
Compare scores to last quarter
Prioritize top 5 debt items
Present to engineering leadership

Annually (Strategic Review)

Review yearly trend data
Benchmark against industry
Set annual debt reduction goals
Budget for major refactoring

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