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By Abdhine Ben Ali | Data Scientist & ML Technical Writer 30/03/2025
Organization: Central African Academy of Cybersecurity
Email: abdhine@acncrca.org
1. Introduction: Why Pipeline Documentation Matters
1. Introduction: Why Pipeline Documentation Matters
In cybersecurity (and most ML applications), 90% of models never reach production due to poor documentation and reproducibility. This guide walks through:
Architecting a maintainable Python pipeline (OOP approach)
Writing ISO-standard documentation for teams
Deploying with monitoring/versioning (MLOps best practices)
“If you didn’t document it, it didn’t happen.” — NASA Systems Engineering Handbook
2. Pipeline Architecture (With UML Diagram)
2. Pipeline Architecture (With UML Diagram)
Figure 1: Pipeline UML (generated with Mermaid)
3. Key Code Implementation
3. Key Code Implementation
3.1 Data Ingestion (Enterprise-Grade)
3.1 Data Ingestion (Enterprise-Grade)
3.2 Advanced Preprocessing
3.2 Advanced Preprocessing
4. Comprehensive Documentation Framework
4. Comprehensive Documentation Framework
4.1 Automated Docs with Sphinx
4.1 Automated Docs with Sphinx
5. Deployment & Monitoring
5. Deployment & Monitoring
FastAPI Endpoint
FastAPI Endpoint
6. Performance Benchmark
6. Performance Benchmark
Resources
Resources
Full Code: GitHub Repo
Dataset: Network Traffic Samples
Pre-Trained Model: Hugging Face Hub
How to Use This Guide:
Engineers: Implement the pipeline using code images
Writers: Reuse the doc templates (Sphinx/Model Cards)
Managers: Share the benchmark table for ROI analysis
Model Selection and Evaluation 2.pdfPage updated
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