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Architecture Documentation

A comprehensive guide to architecture documentation within the context of ai architecture fundamentals.

Documenting AI Architectures

Architecture documentation for AI systems serves two audiences: technical teams who need to understand how the system works, and stakeholders who need to understand what the system does and why it was built this way. Good documentation is the difference between a system that can be maintained and evolved by any qualified engineer and one that requires the original architect to explain every design decision.

In AI systems, documentation is especially important because the system's behavior depends not just on code but on data, model versions, feature definitions, and training configurations. Without documentation, understanding why a model makes certain predictions becomes nearly impossible.

The C4 Model for AI Systems

The C4 model (Context, Containers, Components, Code) by Simon Brown provides an excellent framework for documenting AI architectures at multiple levels of detail.

Level 1: System Context

Shows how the AI system fits into the broader organizational landscape. Who uses it? What external systems does it integrate with? What data sources does it consume?

Level 2: Container Diagram

Shows the major deployable units: the training pipeline, the model serving API, the feature store, the monitoring dashboard, the data lake. Each container has a clear technology choice and responsibility.

Level 3: Component Diagram

Zooms into a single container to show its internal components. For example, the model serving container might contain a request handler, a feature fetcher, a model loader, a prediction cache, and a response formatter.

# AI System Architecture Documentation

## 1. System Context
- Users: Mobile app (5M DAU), internal analytics dashboard
- External: Payment API, user profile service, event stream (Kafka)
- Data sources: PostgreSQL (transactions), S3 (clickstream), Redis

## 2. Containers
| Container | Technology | Purpose |
|-----------|-----------|---------|
| Training Pipeline | Airflow + SageMaker | Daily model retraining |
| Feature Store | Feast + Redis + S3 | Feature serving |
| Model Server | FastAPI + TorchServe | Real-time inference |
| Monitoring | Prometheus + Grafana | System observability |
| Data Lake | Delta Lake on S3 | Raw and processed data |

## 3. Model Server Components
- RequestHandler: Validates input, extracts entity IDs
- FeatureFetcher: Retrieves features from online store
- ModelLoader: Loads versioned model artifacts
- PredictionCache: LRU cache for repeated queries
- ResponseFormatter: Formats output, adds metadata
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Documentation principle: Diagrams should be generated from code or simple text formats (Mermaid, PlantUML, Structurizr DSL) so they stay in sync with the system. Hand-drawn diagrams in Confluence become stale within weeks.

ML-Specific Documentation

Beyond standard software documentation, AI systems need documentation specific to machine learning:

Model Cards

A model card documents a trained model's intended use, performance metrics, limitations, ethical considerations, and evaluation results. Google introduced the concept in 2019 and it has become an industry standard.

Data Sheets for Datasets

Each training dataset should have documentation covering its source, collection methodology, preprocessing steps, known biases, recommended uses, and limitations.

Feature Documentation

  • Name and description — What the feature represents in business terms
  • Computation logic — How the feature is calculated from raw data
  • Data type and range — Expected values and edge cases
  • Freshness SLA — How often the feature is updated
  • Owner — Who is responsible for maintaining the feature
  • Consumers — Which models and systems use this feature

Runbooks and Playbooks

Operational documentation is critical for AI systems because they fail in unique ways:

# Runbook: Model Performance Degradation

## Symptoms
- Model accuracy metric drops below threshold
- Alertmanager fires "model_accuracy_low" alert

## Diagnosis Steps
1. Check data drift dashboard - has input distribution changed?
2. Check feature freshness - are features being updated on schedule?
3. Check recent model deployments - was a new model version deployed?
4. Check upstream data sources - is source data quality degraded?

## Resolution
- If data drift: Trigger model retraining with recent data
- If stale features: Restart feature pipeline, check for errors
- If bad model deployment: Rollback to previous model version
- If source data issue: Contact data team, enable fallback model

Keeping Documentation Current

The biggest challenge with documentation is keeping it current. Several strategies help:

  • Generate from code — Auto-generate API docs, feature catalogs, and model cards from code and metadata
  • Review in PRs — Require documentation updates as part of code review for architectural changes
  • Scheduled audits — Quarterly review of all architecture documentation for accuracy
  • Living documents — Use tools like Backstage or internal wikis that integrate with the development workflow
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Outdated documentation is worse than no documentation because it gives false confidence. If you cannot commit to keeping a document current, do not create it. Focus on the most critical documents and maintain them rigorously.

This completes the AI Architecture Fundamentals course. You now have a solid foundation in the principles, components, decision frameworks, and documentation practices that underpin successful AI architectures. The remaining courses in this category dive deep into specific architecture patterns and technologies.