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The purpose of documentation

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A well-trained model with poor documentation is like a powerful engine without a manual — it works today, but no one knows how to operate, fix or improve it tomorrow.​

Documentation serves multiple purposes, from onboarding a new team member to ensuring regulatory compliance, and it’s a critical component of every ML initiative.

Purpose of documentation banner

Types of documentation​

Different audiences and stages of the ML life cycle require different levels of detail.

  • Project overviews: High-level summaries (READMEs) describing purpose, scope and status.
  • API documentation: Instructions explaining how systems can interact with your model.
  • Model cards: Standardised templates capturing use, performance benchmarks, and ethics.
  • Data dictionaries: Definitions of all features, their data types and descriptions.
  • Maintenance guides: Step-by-step instructions for retraining and monitoring.

Case study: Documentation saves time and reduces risk​

A financial services company faced delays every time its lead data scientist went on leave because critical knowledge existed only in their head.

To address this, the team introduced:

  • README: Explained business purpose and structure.
  • Data dictionary: Defined every variable.
  • Model card: Captured performance and fairness checks.
  • Maintenance guide: Outlined retraining steps.The impact: Reduced reliance on one person, preserved knowledge during staff transitions, and dropped update downtime from weeks to days.

Essential documentation practices​

Good documentation doesn’t have to be time-consuming. Start with a few simple habits:

1. Project overview​

Start with a high-level README file.

  • Purpose: Churn prediction model for subscription customers.
  • Key files: data/, src/, notebooks/.
  • Getting started: Run setup.sh to install dependencies.

2. Data dictionary​

Define every variable to prevent misinterpretation.

  • age: Integer, customer age in years.
  • plan_type: Categorical subscription tier.

3. Code comments​

Explain the why, not just thewhat.

# Dropping 'last_login' because it has >80% missing values and introduces bias
df = df.drop(columns=['last_login'])

Action item: Pause and reflect — stepping into someone else’s shoes​

Picture this: You’ve been asked to take over an ML project halfway through. You open the repository and notice gaps — no clear guide, cryptic features, and unclear preprocessing steps.

  • What information would you hope to find to help you move forward?
  • How could having that documentation save you time or reduce frustration?

Information you'd hope to find:

Benefits of having documentation: