Practical Interpretability - Learning Module

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Documentation

The Foundation of Sustainable Interpretability

Interpretability methods can extract insights from models, visualizations can communicate patterns, and explanations can satisfy stakeholder questions. But without comprehensive documentation, all these capabilities degrade over time. Team members leave. Contexts change. Memory fades. What seemed obvious during development becomes mysterious six months later.

Documentation is the institutional memory of machine learning. It transforms individual knowledge into organizational capability, ensures continuity across team transitions, enables audit and accountability, and provides the foundation for continuous improvement. Without documentation, every team confronting an ML system must rediscover what previous teams learned—often after problems have occurred.

This page examines documentation as a comprehensive practice: what to document, when to document it, how to maintain living documentation, and how to build organizational systems that make documentation sustainable rather than burdensome.

What You Will Learn

By the end of this page, you will understand comprehensive ML documentation architecture, lifecycle-appropriate documentation practices, documentation governance and maintenance strategies, and practical approaches to making documentation sustainable and valuable.

The Documentation Architecture

Effective ML documentation isn't a single document—it's an architecture of interconnected artifacts serving different purposes and audiences. Understanding this architecture enables strategic documentation that maximizes value while minimizing redundancy.

The Documentation Pyramid:

ML Documentation Layers
Layer	Purpose	Audience	Update Frequency
Summary Layer	Quick understanding, decision support	Executives, auditors, product teams	Major changes only
Operational Layer	Day-to-day use, monitoring, incidents	ML ops, on-call engineers, support	With operational changes
Technical Layer	Deep understanding, debugging, improvement	ML engineers, reviewers, researchers	With model changes
Provenance Layer	Audit trail, compliance, investigation	Auditors, legal, compliance	Immutable, append-only
Research Layer	Exploration, experimentation, learning	R&D, advanced ML practitioners	Continuous experimentation

Key Artifact Types:

Core Documentation Artifacts

•Model Card: Standardized summary (covered previously)—serves the summary layer
•Technical Specification: Comprehensive architecture, algorithms, parameters, feature definitions—serves the technical layer
•Data Documentation: Sources, schemas, preprocessing, quality, lineage—bridges technical and provenance layers
•Validation Report: Evaluation methodology, results, subgroup analysis, calibration—serves technical and summary layers
•Operational Runbook: Deployment, monitoring, alerting, incident response, retraining procedures—serves operational layer
•Decision Log: Key choices, rationale, alternatives considered, risk acceptance—serves provenance layer
•Experiment Records: Hypotheses tested, results, learnings—serves research layer
•Change Log: Version history, what changed, why, testing performed—bridges all layers

The Cross-Reference Principle:

Documents should reference each other rather than duplicate content:

Model Card
├── References Technical Specification for architecture details
├── References Validation Report for complete metrics
├── References Data Documentation for training data details
└── Links to Operational Runbook for deployment information

Technical Specification
├── References Data Documentation for feature sources
├── References Experiment Records for design choices
└── Links to Change Log for version history

Operational Runbook
├── References Model Card for model behavior summary
├── References Technical Specification for troubleshooting
└── Links to Decision Log for operational decisions

This reduces duplication, ensures consistency, and allows appropriate detail at each level.

Single Source of Truth

Every fact should live in exactly one document. Other documents reference it. Duplication creates inconsistency—when updates happen, some copies get missed. Reference liberally; duplicate never.

What to Document

Comprehensive ML documentation requires capturing information across multiple dimensions. Here's a systematic breakdown of documentation content.

Intent and Context Documentation:

Document the why before the what:

Problem Definition

What business/user problem does this model address?
What decisions will the model inform or automate?
Who are the affected stakeholders?
What are the consequences of model errors (false positives, false negatives)?
What alternatives were considered (non-ML solutions, different ML approaches)?

Success Criteria

How will success be measured?
What metrics indicate adequate performance?
What thresholds trigger concern or action?
How does model success connect to business outcomes?

Constraints

What constraints shape the solution? (latency, compute, interpretability, regulatory)
What tradeoffs were made and why?
What compromises exist between ideal and implemented?

Scope

What is in scope for this model?
What is explicitly out of scope?
What populations, use cases, or scenarios are not covered?

Historical Context

What preceded this model? (earlier versions, different approaches)
What problems with the previous approach motivated this one?
What was learned from prior attempts?

Lifecycle Documentation

Different ML lifecycle phases have different documentation needs. Matching documentation activities to lifecycle phases ensures coverage without overwhelming teams.

Documentation by Lifecycle Phase:

ML Lifecycle Documentation Activities
Phase	Primary Documentation	Documentation Focus
Problem Scoping	Problem definition, success criteria, constraints	Why are we building this? What does success look like?
Data Collection	Data sources, collection methods, consent	What data do we have? How was it obtained?
Data Preparation	Preprocessing pipeline, feature engineering	How is raw data transformed to model inputs?
Exploration	Experiment records, EDA findings	What did we learn about the data and problem?
Model Development	Architecture decisions, training procedures	What approach are we taking and why?
Validation	Evaluation methodology, results, subgroup analysis	How well does this work? For whom?
Pre-Deployment	Model Card, operational runbook, risk assessment	Is this ready for production? What's the plan?
Deployment	Deployment records, configuration, monitoring setup	How was this deployed? What's being monitored?
Production	Monitoring logs, incident records, performance trends	How is it working? What problems have occurred?
Maintenance	Retraining records, version history, improvement log	How has this evolved? What's been learned?
Retirement	Retirement rationale, migration plan, archived records	Why is this ending? What happens to dependencies?

The Documentation-as-You-Go Principle:

Documentation created later is fundamentally inferior to documentation created during the work:

Created During	Created After
Captures actual reasoning	Reconstructs reasoning (often incorrectly)
Includes abandoned paths	Only shows what was chosen
Reflects uncertainty and debates	Presents artificial certainty
Contemporary evidence	Appears reconstructed for convenience
Details remembered	Details forgotten

Making Documentation Sustainable:

Documentation often fails because it's treated as overhead rather than integral work. Strategies for sustainability:

Template-Based: Pre-defined templates reduce the cognitive load of deciding what to document
Pipeline-Integrated: Automated capture of metrics, parameters, and lineage as side effects of running code
Review-Enforced: Documentation completeness as a criterion for phase gates and deployments
Tooling-Supported: Dedicated tools for documentation rather than generic wikis

The Reconstruction Trap

When documentation is created pre-audit or pre-deployment as a formality, it's reconstruction, not documentation. Reconstructed documentation misses nuance, omits problems, and reflects desired narratives rather than reality. Auditors and investigators can often tell the difference.

Technical Specification Deep Dive

The Technical Specification is the authoritative detailed document for model architecture, training, and behavior. It serves ML engineers who need to understand, debug, or improve the system.

Technical Specification Structure:

Technical Specification Template
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# Technical Specification: [Model Name] v[X.Y.Z]
 
## 1. Executive Summary
- **Purpose:** One-paragraph description of what this model does
- **Key Metrics:** Primary performance metrics with values
- **Critical Limitations:** Top 3 limitations to keep in mind
- **Detailed Model Card:** [Link]
 
## 2. Architecture
 
### 2.1 Model Type and Structure
- Algorithm family and specific implementation
- Architecture diagram (if complex)
- Number of parameters/complexity measures
 
### 2.2 Hyperparameters
| Parameter | Value | Rationale |
|-----------|-------|-----------|
| [name] | [value] | [why this value] |
 
### 2.3 Dependencies
- Software dependencies with versions
- External model dependencies
- Data dependencies
 
## 3. Features
 
### 3.1 Feature Inventory
| Feature Name | Type | Source | Description | Importance |
|--------------|------|--------|-------------|------------|
| [name] | [numeric/categorical/...] | [source system] | [what it means] | [rank/score] |
 
### 3.2 Feature Engineering Pipeline
- Diagram of preprocessing flow
- Transformation specifications
- Handling of missing values
- Encoding methods for categorical features
 
### 3.3 Feature Interactions
- Known important interactions
- Interaction constraints (if any)
 
## 4. Training
 
### 4.1 Training Data
- **Source:** [Data source reference]
- **Time Period:** [Date range]
- **Volume:** [Number of samples]
- **Class Distribution:** [For classification]
- **Data Specification:** [Link to Data Documentation]
 
### 4.2 Training Procedure
- Objective function/loss
- Optimization algorithm
- Learning rate schedule
- Regularization
- Early stopping criteria
- Cross-validation strategy
 
### 4.3 Training Infrastructure
- Hardware used
- Training duration
- Memory requirements
 
### 4.4 Reproducibility
- Random seeds
- Environment specification (requirements.txt, Docker image)
- Training script location
 
## 5. Evaluation
 
### 5.1 Evaluation Data
- Source and construction
- Time period and volume
- Relationship to training data (leakage prevention)
 
### 5.2 Metrics
| Metric | Value | 95% CI | Note |
|--------|-------|--------|------|
| [name] | [value] | [interval] | [context] |
 
### 5.3 Disaggregated Performance
- Performance by [factor 1]
- Performance by [factor 2]
- Intersectional analysis
 
### 5.4 Calibration
- Calibration method (if applied)
- Reliability diagram
- Calibration metrics
 
### 5.5 Error Analysis
- Common error patterns
- Edge case behavior
- Confidence vs accuracy relationship
 
## 6. Inference
 
### 6.1 Input Specification
- Expected input format
- Required preprocessing
- Input validation
 
### 6.2 Output Specification
- Output format and interpretation
- Probability calibration note
- Decision threshold (if applicable)
 
### 6.3 Performance Characteristics
- Latency (p50, p95, p99)
- Memory footprint
- Throughput capacity
 
## 7. Limitations
 
### 7.1 Known Failure Modes
| Scenario | Behavior | Mitigation |
|----------|----------|------------|
| [case] | [what happens] | [what to do] |
 
### 7.2 Out-of-Distribution Behavior
- How model behaves on novel inputs
- Distribution shift sensitivity
 
### 7.3 Uncertainty Quantification
- Confidence score interpretation
- When confidence is unreliable
 
## 8. Version History
 
| Version | Date | Changes | Author |
|---------|------|---------|--------|
| [X.Y.Z] | [date] | [what changed] | [who] |
 
## 9. References
 
- Related documents: [links]
- Research papers: [citations]
- Code repositories: [links]
 
## 10. Appendices
 
### A. Detailed Feature Definitions
[Complete feature dictionary]
 
### B. Training Configuration
[Exact configuration files used]
 
### C. Evaluation Methodology Details
[Complete evaluation protocol]

Living Documents

Technical specifications should be version-controlled alongside the code. Every model version should have a corresponding specification version. Changes to the model should trigger specification updates. Outdated specifications are worse than no specifications—they actively mislead.

Operational Runbooks

Operational runbooks enable anyone—including on-call engineers who didn't build the model—to operate, monitor, troubleshoot, and maintain ML systems. They're written for someone at 3 AM who needs to determine if a problem is real and what to do about it.

Runbook Design Principles:

Runbook Best Practices

•Action-Oriented: Each section should answer 'what do I do?' not just 'what is this?'
•Decision Trees: For troubleshooting, provide clear if-then logic: 'If X, then Y'
•Copy-Paste Ready: Commands should be directly usable, not requiring interpretation
•Escalation Clear: When and to whom to escalate is unambiguous
•Tested: Procedures should be periodically tested to ensure they work
•Updated After Incidents: Post-incident reviews should update runbooks with new knowledge

Operational Runbook Template
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# Operational Runbook: [Model Name]
 
## Quick Reference
 
| Item | Value |
|------|-------|
| **Model** | [Name and version] |
| **Service** | [Service name/URL] |
| **Dashboard** | [Monitoring dashboard link] |
| **Logs** | [Log query/location] |
| **On-Call** | [Team/rotation] |
| **Escalation** | [Escalation path] |
| **Last Updated** | [Date] |
 
---
 
## 1. Service Overview
 
**What This Does:**
[One paragraph description of what the model/service does]
 
**Business Impact:**
[What happens to users/business if this fails?]
 
**Health Check:**
```bash
# Quick health check command
curl -s https://service/health | jq .
```
 
---
 
## 2. Monitoring & Alerts
 
### 2.1 Key Metrics
 
| Metric | Normal Range | Warning | Critical | Dashboard |
|--------|--------------|---------|----------|-----------|
| Prediction latency (p95) | < 100ms | > 200ms | > 500ms | [link] |
| Prediction volume | 1K-10K/min | < 500/min | < 100/min | [link] |
| Error rate | < 0.1% | > 1% | > 5% | [link] |
| Model confidence (mean) | 0.6-0.9 | < 0.5 | < 0.3 | [link] |
| Prediction distribution | [baseline] | drift > 0.1 | drift > 0.2 | [link] |
 
### 2.2 Active Alerts
 
| Alert | Severity | Meaning | Action |
|-------|----------|---------|--------|
| HighLatencyAlert | Warning | p95 latency elevated | See §3.1 |
| LowVolumeAlert | Warning | Prediction volume dropped | See §3.2 |
| HighErrorRate | Critical | Error rate elevated | See §3.3 |
| ModelDrift | Warning | Prediction distribution shifted | See §3.4 |
 
---
 
## 3. Troubleshooting
 
### 3.1 High Latency
 
**Symptoms:** HighLatencyAlert firing, slow response times
 
**Diagnosis:**
1. Check CPU/memory utilization: [dashboard link]
2. Check request queue depth: [metric]
3. Check upstream dependency latency: [dashboard link]
4. Check for unusual request patterns (size, frequency)
 
**If Cause is...**
 
| Cause | Action |
|-------|--------|
| High CPU/memory | Scale up replicas: [procedure link] |
| Dependency slow | Check dependency status; escalate to [team] |
| Traffic spike | Verify legitimate; consider rate limiting |
| Memory leak | Restart pods: `kubectl rollout restart...` |
 
### 3.2 Low Volume
 
**Symptoms:** LowVolumeAlert firing, prediction volume below threshold
 
**Diagnosis:**
1. Is upstream service sending traffic? [check]
2. Are health checks passing? [check]
3. Is there a deployment in progress? [check]
4. Is there a broader outage? [check]
 
**Most Common Causes:**
1. Upstream service outage
2. Network/routing issue
3. Deployment misconfiguration
 
[...]
 
### 3.3 High Error Rate
 
### 3.4 Model Drift
 
---
 
## 4. Common Operations
 
### 4.1 Rollback to Previous Version
 
**When to Use:** Critical issue with current version; need immediate revert
 
**Procedure:**
```bash
# 1. Identify last known good version
kubectl get replicasets -n ml-models | grep model-name
 
# 2. Rollback
kubectl rollout undo deployment/model-name -n ml-models
 
# 3. Verify rollback
kubectl get pods -n ml-models | grep model-name
 
# 4. Confirm health
curl -s https://service/health | jq .
```
 
**Post-Rollback:**
- Create incident ticket
- Notify [stakeholders]
- Schedule root cause analysis
 
### 4.2 Scale Replicas
 
### 4.3 Manual Retraining
 
### 4.4 Feature Store Refresh
 
---
 
## 5. Incident Response
 
### 5.1 Severity Definitions
 
| Severity | Definition | Response Time | Examples |
|----------|------------|---------------|----------|
| SEV1 | Complete outage | Immediate | Service down; all predictions failing |
| SEV2 | Major degradation | 15 min | 50%+ requests failing; severe latency |
| SEV3 | Minor degradation | 1 hour | Elevated errors; some latency |
| SEV4 | Inconvenience | 24 hours | Minor issues; no user impact |
 
### 5.2 Escalation Matrix
 
| Issue Type | First Contact | Escalation 1 | Escalation 2 |
|------------|---------------|--------------|--------------|
| Service outage | On-call ML Eng | ML Team Lead | VP Engineering |
| Data issues | Data Platform | Data Eng Lead | - |
| Model accuracy | ML Team | Product Owner | - |
 
### 5.3 Communication Templates
 
**Incident Start:**
> [INCIDENT] [Model Name] - [Brief description]. 
> Severity: [SEV#]. Impact: [user impact].
> Investigating. Updates every [X] minutes.
 
**Incident Resolution:**
> [RESOLVED] [Model Name] - [What was wrong]. 
> Root cause: [brief]. Duration: [X minutes/hours].
> Post-mortem scheduled: [date].
 
---
 
## 6. Maintenance Procedures
 
### 6.1 Scheduled Retraining
- **Schedule:** [Weekly/Monthly/etc]
- **Procedure:** [Link to retraining SOP]
- **Validation Required:** [What checks before deploy?]
- **Rollback Criteria:** [When to abort?]
 
### 6.2 Model Validation
- **Frequency:** [Schedule]
- **Metrics Checked:** [List]
- **Thresholds:** [Values]
- **Actions if Failed:** [Procedure]
 
---
 
## 7. Contacts
 
| Role | Name | Contact |
|------|------|---------|
| Primary On-Call | [Rotation] | [PagerDuty/Slack] |
| ML Team Lead | [Name] | [email/phone] |
| Data Platform | [Name] | [email/slack] |
| Product Owner | [Name] | [email/slack] |
 
---
 
## Change Log
 
| Date | Author | Change |
|------|--------|--------|
| [date] | [name] | [what changed] |

Decision Logs and Governance

Decision logs capture the reasoning behind key choices—creating institutional memory that survives team transitions and enables learning from past decisions. Combined with governance structures, they ensure accountability and consistency.

Why Decision Documentation Matters:

With Decision Logs

•New team members understand why things are the way they are
•Similar decisions benefit from past reasoning
•Mistakes aren't repeated—lessons are captured
•Auditors understand decision-making process
•Accountability is clear and traceable

Without Decision Logs

•"Why did we do this?" is unanswerable
•Each team rediscovers the same constraints
•Mistakes are repeated across projects
•Auditors question the decision process
•No one knows who decided what or when

Architecture Decision Records (ADRs) for ML:

Borrowing from software engineering, Architecture Decision Records can be adapted for ML:

# ADR-042: Selection of XGBoost over Neural Network for Credit Scoring

## Status
Accepted

## Date
2024-01-10

## Context
We need to select a model architecture for the updated credit scoring model. 
Constraints include:
- Regulatory requirement for explainability (SR 11-7 compliance)
- Latency requirement < 50ms for real-time scoring
- Accuracy competitive with current model (AUC > 0.82)
- Team capacity to maintain and monitor

## Decision
We will use XGBoost gradient boosted trees rather than neural networks.

## Alternatives Considered
1. **Neural Network (MLP):** Rejected - insufficient interpretability for regulatory 
   requirements; would require post-hoc explanation methods that are contested.
2. **Logistic Regression:** Rejected - did not meet accuracy threshold in experiments 
   (AUC 0.79 vs 0.85 for XGBoost).
3. **Random Forest:** Considered acceptable, but XGBoost showed better performance 
   and has mature SHAP support.

## Consequences
Positive:
- Native feature importance and SHAP explanations
- Met latency requirements (p95 < 20ms)
- Exceeded accuracy threshold (AUC 0.85)

Negative:
- May not capture complex nonlinear patterns as well as neural network
- Hyperparameter tuning more sensitive

## Related Decisions
- ADR-039: Regulatory compliance framework
- ADR-041: Interpretability tooling selection (SHAP)

## Authors
[Names], [Role]

Governance Structures:

Documentation requires governance—roles, responsibilities, and processes that ensure documentation happens and stays current:

Role	Responsibility
Model Owner	Accountable for model documentation completeness and accuracy
Documentation Lead	Sets standards, reviews quality, maintains templates
Technical Reviewer	Validates technical accuracy of documentation
Process Auditor	Periodically verifies documentation compliance
Executive Sponsor	Provides authority and resources for documentation program

Governance Mechanisms:

Deployment Gates: No production deployment without documentation checkpoints
Periodic Audits: Random sample of models checked for documentation quality
Staleness Alerts: Automated detection of outdated documentation
Ownership Registry: Every model has assigned accountable owner
Template Enforcement: Tooling that enforces required documentation sections

Lightweight Governance

Governance should enable, not impede. Heavy processes create workarounds. Aim for lightweight mechanisms that are easy to follow and hard to skip—automation helps more than bureaucracy.

Documentation Tooling

Appropriate tooling makes documentation sustainable. Poor tooling makes it burdensome. Here's a landscape of tools that support ML documentation.

ML Documentation Tool Categories
Category	Examples	Use Case
Experiment Tracking	MLflow, Weights & Biases, Neptune, ClearML	Automatic capture of training runs, parameters, metrics
Model Registry	MLflow Registry, SageMaker Model Registry, Vertex AI	Model versioning with attached metadata and documentation
Data Catalogs	Apache Atlas, DataHub, Alation, AWS Glue Catalog	Data discovery, lineage, quality documentation
Model Cards	Model Card Toolkit, Hugging Face Model Cards	Standardized model documentation generation
Documentation Platforms	Confluence, Notion, GitBook, ReadTheDocs	General documentation hosting with collaboration
Version Control	Git + Markdown, GitHub/GitLab Wikis	Version-controlled documentation alongside code
Feature Stores	Feast, Tecton, Hopsworks	Feature documentation with lineage and statistics
Pipeline Tools	Kubeflow, Airflow, Prefect	Pipeline documentation and execution logs

Integration Strategies:

1. Docs-as-Code

Treat documentation like code:

Documentation in Git alongside model code
Same PR/review process for doc changes
CI checks for documentation completeness
Automated publishing on merge

Benefits: Version control, review process, lives with code Drawbacks: Requires engineer-friendly tooling; less accessible to non-engineers

2. Centralized Platform

Use a dedicated documentation platform:

Confluence, Notion, or similar wiki
All ML documentation in one searchable location
Rich formatting, embedding, linking

Benefits: Accessible to all; rich editing Drawbacks: Can drift from code; versioning harder

3. Registry-Centric

Model registry as documentation hub:

Model versioning inherently tracks documentation
Metadata attached to model artifacts
Single source of truth for deployed models

Benefits: Documentation tied to artifacts; clear versioning Drawbacks: May not support all documentation types

4. Automated Capture

Maximize automated documentation:

Experiment tracking captures parameters and metrics automatically
Pipeline systems log execution details
Data catalogs capture schema and statistics automatically

Benefits: Low manual effort; contemporaneous Drawbacks: Doesn't capture intent, decisions, or context

The Hybrid Approach

Most mature organizations use a hybrid: automated capture for what can be automated, structured templates for what needs human input, and integration points that link different systems. The goal is documentation that's easy to create, easy to find, and easy to trust.

Common Documentation Failures

Understanding common failure modes helps avoid them. Here are patterns that undermine documentation programs:

Documentation Anti-Patterns

•The One-and-Done: Documentation created at launch, never updated. Becomes misleading within months as the system evolves. Fix: Define update triggers; audit for staleness.
•The Kitchen Sink: Documentation so comprehensive no one reads it. Important information buried in pages of irrelevant detail. Fix: Layer documentation; summary first, details by reference.
•The Ghost Town: Documentation platform exists but is barely used. Critical information lives in Slack, email, and memory. Fix: Make documentation the path of least resistance; integrate into workflow.
•The Hero Dependency: One person writes all documentation. When they leave, documentation stops. Fix: Distribute responsibility; documentation as team norm, not individual heroism.
•The Copy-Paste: Same generic text across different systems. Provides false sense of documentation without actual value. Fix: Template for structure; require specific content.
•The Wishful Thinking: Documentation describes how things should work, not how they do work. Dangerous for troubleshooting and auditing. Fix: Require validation; periodic accuracy checks.
•The Scattered Fragments: Documentation spread across wikis, repos, tickets, and email. No one can find anything. Fix: Single discovery point; clear organization; linking between systems.

Diagnosing Documentation Health:

Periodically assess your documentation program:

Question	Good Sign	Warning Sign
How do new team members learn about models?	They read documentation	They ask the original developer
When something breaks, what happens?	Runbook is consulted	Frantic Slack searching
When was documentation last updated?	Within appropriate cadence	"Probably years ago"
Can you answer auditor questions from docs?	Yes, quickly	Need to investigate
Is there one place to find documentation?	Yes, well-organized	It's in several places, maybe
Do teams document without prompting?	Yes, it's normal	Only when required by gate

Recovery Strategies:

If documentation is in poor state:

Prioritize high-risk models: Start documentation effort where it matters most
Accept technical debt: Document current state, not the perfect state you wish existed
Integrate forward: New work follows documentation requirements; backfill gradually
Make it easier: If documentation is too hard, simplify templates and tooling
Celebrate good examples: Highlight teams doing documentation well

Documentation Debt Compounds

Like technical debt, documentation debt gets worse over time. Each passing month makes reconstruction harder as knowledge dissipates. Invest in documentation prevention, not just documentation remediation.

Summary: Documentation

Documentation transforms individual knowledge into organizational capability, enabling continuity, auditability, and continuous improvement throughout the ML lifecycle. Let's consolidate the key insights:

Key Takeaways

•Documentation is architecture, not a single document — Multiple interconnected artifacts serve different audiences and purposes.
•Document intent, data, model, operations, and decisions — Comprehensive coverage requires capturing each dimension.
•Match documentation to lifecycle phase — Different phases need different documentation; document as you go.
•Technical specifications provide depth — Comprehensive architecture, training, and behavior documentation for ML engineers.
•Runbooks enable operations — Action-oriented, copy-paste-ready documentation for anyone operating the system.
•Decision logs create institutional memory — Capture why, not just what, to enable learning and avoid repeating mistakes.
•Tooling should make documentation easy — Automation, integration, and appropriate platforms reduce burden.
•Avoid common failures — One-and-done, kitchen sink, scattered fragments, and other anti-patterns undermine documentation value.

What's Next:

Documentation provides the foundation, but ongoing assurance requires active auditing. The final page examines Auditing practices—systematic evaluation of ML systems against requirements, standards, and expectations to ensure interpretability and fairness commitments are maintained throughout the system lifecycle.

Page Complete

You now understand comprehensive ML documentation practices. Remember: documentation is an investment in the future—your future self, your teammates, your successors, and your stakeholders will thank you for documentation created thoughtfully today. Next, we'll explore auditing practices.