README

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iso_doc_type: "Description" iso_viewpoint: "ComponentView" type: "module" title: "MLOps Python Package — Documentation Guide" description: "Repository documentation guide and technical overview for the MLOps Python Package." tags: ["iso42010", "okf", "guide", "readme"] timestamp: "2026-10-01T17:50:00Z" generated: "agent:wiki-iso-documentation-standard" verified: "true" last_verified_commit: "073b5fd"

MLOps Python Package — Documentation Guide

Source Reference: Anchor at repository root .
Layer: Systems Documentation
Role: Comprehensive guide to repository layout, developer setup, code metrics, and ISO compliance standards.

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1. Executive Overview

The MLOps Python Package (regression_model_template) is an enterprise-grade reference architecture for reproducible, scalable, and secure machine learning operations. It decouples domain logic, data contracts, and ML algorithms from I/O mechanisms, external tracking services, and streaming infrastructure.

Core Technology Stack

Component	Technology	Rationale
Programming Language	Python 3.10+	Strict type hinting, standard typing syntax.
Package & Env Manager	Poetry	Deterministic dependency resolution, virtualenv management.
Data Contract Validation	Pandera + Pydantic v2	Strict tabular schema contracts, typing validation.
ML Algorithms & Evaluation	Scikit-Learn	Extensible model interfaces (BaselineSklearnModel, SklearnMetric).
Hyperparameter Search	Optuna	Bayesian hyperparameter optimization and pruning.
Explainability (XAI)	SHAP (TreeExplainer)	Global and local feature attribution extraction.
Experiment Tracking	MLflow	Metric, parameter, and model registry lifecycle management.
Streaming & API	FastAPI + Confluent Kafka	High-throughput streaming inference with sliding-window rate limiting.
Data Versioning	DVC + Parquet	Immutable dataset versioning and efficient columnar storage.
Task Automation	Invoke (tasks/)	Standardized execution targets for linting, testing, and Docker builds.

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2. Repository Layout

mlops-python-package/
├── confs/                      # Hierarchical OmegaConf YAML configurations
├── data/                       # DVC-tracked datasets (raw, interim, processed)
├── notebooks/                  # Exploratory data analysis notebooks
├── src/
│   └── regression_model_template/  # Primary Python package
│       ├── controller/         # Streaming API & Kafka endpoints
│       ├── core/               # Mathematical domain: Models, Metrics, Schemas
│       ├── io/                 # Input/Output: Configs, Datasets, Registries, Services
│       ├── jobs/               # Workflow execution: Train, Tune, Eval, Explain, Infer, Promote
│       ├── utils/              # Auxiliary utilities: Searchers, Signers, Splitters
│       ├── init_data.py        # Synthetic data generation utility
│       ├── scripts.py          # Script execution entrypoint
│       └── settings.py         # Application settings
├── tasks/                      # Invoke task runners
├── tests/                      # 29 Pytest unit and integration test suites
└── wiki/                       # ISO-compliant GitHub wiki documentation

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3. Codebase Metrics Summary

• Total Python Modules: 29
• Total Unit & Integration Test Suites: 29
• Line Coverage Target: ≥ 95%
• AST Verified Symbols: 25+ classes, 60+ methods, 100% typed contracts
• Broken Relative Links: 0 (Automated link resolution gate)