9. Architecture Decisions

This chapter documents key architectural decisions made during the design and implementation of the system.

architecture_decisions

Each decision follows a structured format to capture important details:

Title – A short, descriptive name for the decision.
Context – The problem, background, and reasoning for the decision.
Decision – The chosen solution and justification.
Consequences – The impact of the decision, including trade-offs and alternatives considered.

9.1 FastAPI for Backend Services

Title: FastAPI for Backend Services
Context: The backend requires a high-performance, async-first API framework for handling machine learning workloads.
Decision: We selected FastAPI due to its automatic OpenAPI documentation, async support, and performance optimizations.
Consequences:

  • Faster response times with async I/O

  • Built-in validation via Pydantic

  • Requires developer familiarity with async paradigms

9.2 Deployment Strategy: Docker

Title: Deployment Strategy: Docker
Context: The system needs a scalable, containerized deployment approach that supports multi-environment staging and production.
Decision: We use Docker for containerization for orchestration, ensuring portability and scalability.
Consequences:

  • Simplified dependency management with Docker

  • Easier Deployment (independent services)

  • More complexity

9.3 Database Choice: PostgreSQL

Title: Database Choice: PostgreSQL
Context: The system requires a reliable, ACID-compliant database to store structured data.
Decision: We chose PostgreSQL due to its robust transactions, scalability, and strong SQL support.
Consequences:

  • Supports advanced queries and analytics

  • Open-source with strong community support

  • Requires tuning for large-scale ML workloads

9.4 Storage: MinIO as S3-Compatible Object Store

Title: Storage: MinIO as S3-Compatible Object Store
Context: Machine learning workflows need a scalable, durable storage solution for datasets.
Decision: We use MinIO, an S3-compatible storage service, for fast, distributed object storage.
Consequences:

  • Works seamlessly with MLflow for model tracking

  • Scalable and deployable on-premise

  • Requires additional backup strategies for data persistence

9.5 Monitoring & Logging: Prometheus + Grafana + Loki

Title: Monitoring & Logging: Prometheus + Grafana + Loki
Context: The system requires comprehensive observability to detect failures and monitor performance.
Decision:

  • Prometheus for metrics collection

  • Grafana for visualization

  • Loki for centralized logging

Consequences:

  • Unified monitoring stack improves debugging

  • Customizable dashboards for system health

  • Additional infrastructure overhead

9.5 CI/CD Strategy: GitHub Actions for Automation

Title: CI/CD Strategy: GitHub Actions for Automation
Context: Automated testing, building, and deployment are required for continuous integration and delivery.
Decision: We use GitHub Actions for automating tests, builds, and deployments.
Consequences:

  • Faster release cycles with automated testing

  • Seamless Git-based workflow integration

  • Requires maintaining workflow configurations

9.6 API Gateway & Reverse Proxy: Nginx

Title: API Gateway & Reverse Proxy: Nginx
Context: We need a secure entry point for incoming requests and SSL termination for public services.
Decision: Nginx is used as a reverse proxy and load balancer.
Consequences:

  • Improved security with rate limiting and DDoS protection

  • Load balancing across backend services

  • Requires ongoing configuration updates