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Training Reproducibility Guide

Recommended ml training_reproducibility
Agent Prompt Snippet
Document all random seeds, library versions, hardware specifications, and hyperparameters needed to fully reproduce the model training process.

Purpose

A reproducibility guide records random seeds, library versions, hardware specs, and hyperparameters so that any engineer can reproduce training results from scratch.

This is a Recommended document — most projects benefit significantly from having one. While not strictly essential for every situation, its absence often leads to gaps in team understanding or quality.

Key Sections to Include

  • All random seeds
  • Library versions
  • Hardware specifications
  • Hyperparameters needed to fully reproduce the model training process

Agent hint: Document all random seeds, library versions, hardware specifications, and hyperparameters needed to fully reproduce the model training process.

What Makes It Good vs Bad

A strong version of this document:

  • Documents model architecture, training data, and evaluation metrics clearly
  • Includes bias analysis and fairness considerations
  • Specifies model versioning, A/B testing, and rollback procedures
  • Defines monitoring for model drift, data drift, and performance degradation
  • Connects model decisions to business outcomes with measurable criteria

Warning signs of a weak version:

  • Only documents final model — no record of experiments or alternatives tried
  • Missing bias and fairness analysis for the training data and predictions
  • No monitoring strategy for production model performance
  • Training pipeline undocumented — impossible to reproduce results
  • No clear process for model updates, retraining triggers, or deprecation

Common Mistakes

  • Not documenting the training data provenance and preprocessing steps
  • Skipping fairness and bias analysis — assuming the data is representative
  • Deploying models without monitoring for performance degradation over time
  • Treating model training as a one-time event rather than a recurring process

How to Use This Document

Document the full ML lifecycle: data collection, preprocessing, feature engineering, model selection, training, evaluation, deployment, and monitoring. Record experiment results even for failed approaches — they prevent future teams from repeating dead ends. Define clear criteria for when a model should be retrained or retired.

For AI agents: Reference ML documentation to understand model behavior, training data characteristics, and known limitations. When modifying ML pipelines, verify changes against documented evaluation metrics and fairness criteria.

Starter Template

SpecBase includes a ready-to-use template for this document: kb/templates/ml/training_reproducibility.md.tmpl. Use the SpecBase CLI or MCP integration to generate it pre-filled for your project.

# Generate stubs via CLI
specbase init <archetype> --features <features> --dir ./docs
  • Designing Machine Learning Systems by Chip Huyen — End-to-end guide to ML system design covering data, training, deployment, and monitoring.
  • Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson & Michael Munn — Reusable solutions to common challenges in ML engineering and architecture.
  • Responsible AI in Practice by Yolanda Gil — Framework for ethical AI development including fairness, transparency, and accountability.

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