📄 How to Write an ML Design Doc

Gradient Descent Weekly — Issue #19

Machine learning projects fail quietly.
Not with an explosion — but with a whimper after months of vague goals, unclear ownership, and untested assumptions.

What’s the antidote?

A clear, detailed, brutally honest ML design document — written before you start coding models, wiring pipelines, or tuning hyperparameters.

In this issue, we’ll break down:

• Why ML design docs are critical

• What to include (and what to skip)

• Real-world examples and formats

• How to review them without turning it into a 4-week process

🧠 Why You Need an ML Design Doc

Software engineering has PRDs. Architecture reviews. RFCs.
ML? Too often we skip right to code — and pay the price later.

Writing a design doc:

✅ Forces you to think through edge cases
✅ Exposes data problems before training starts
✅ Aligns stakeholders on goals, risks, and definitions
✅ Creates a paper trail for future audits
✅ Accelerates onboarding and handoffs

ML isn’t just science. It’s engineering. And good engineering starts on paper.

📑 Core Sections of an ML Design Doc

Here’s a battle-tested outline used by FAANG, unicorns, and top MLOps teams:

🧭 1. Problem Statement

• What are we trying to solve?

• Who is impacted (internal/external users)?

• What’s the business objective or KPI?

🔍 Example: “Detect fraudulent transactions in near real-time to reduce false chargebacks by 30%.”

🗃️ 2. Data Sources

• Where is the data coming from?

• What is the expected data volume/velocity?

• How will data be accessed? (API, batch, DB)

Include:

• Schema samples

• Known quality issues

• Frequency of updates

• Access limitations or privacy considerations

🧪 3. Labels & Ground Truth

• How are labels generated?

• Are they manually verified, derived, or inferred?

• How confident are we in their correctness?

✅ Include: lag time, label leakage risks, class imbalance issues

🧠 4. Modeling Approach

• What kind of model(s) are you planning to use? Why?

• Are you reusing an existing model? Fine-tuning?

• What are the baselines?

Don’t obsess over architecture here. Just explain the why, not the how.

🧮 5. Evaluation Metrics

• What metrics matter most (to the business)?
  e.g., accuracy, F1, precision at K, RMSE, AUC, latency, recall

• What’s the minimum acceptable performance?

• How will we evaluate (cross-validation, time-split, holdout)?

• Any segment-wise evaluation (e.g. new users vs power users)?

⚙️ 6. Pipeline Architecture

• Diagram of the end-to-end flow: from raw data to prediction

• Training pipeline: preprocessing, training, validation

• Inference pipeline: real-time vs batch, deployment format

• Tools/libraries/frameworks used (e.g., MLflow, SageMaker, DVC)

📌 Tip: Include how models are versioned, stored, and monitored.

🔐 7. Risks & Assumptions

• What could go wrong? (Data issues, drift, abuse, edge cases)

• What assumptions are being made? (Static schema? Feature stability?)

This is where mature teams separate themselves. Get honest. Be paranoid.

🚨 8. Monitoring Plan

• What metrics will be tracked in production?

• How will you detect drift, performance decay, data anomalies?

• What’s the alerting mechanism?

• Who gets paged?

Include retraining triggers and rollback strategies.

📅 9. Timeline & Milestones

Break it down:

• Data analysis: [Start – End]

• Modeling prototype: [Start – End]

• Deployment: [Start – End]

• Evaluation checkpoint: [Date]

• Go/No-Go: [Date]

Attach deadlines, even if rough. It adds pressure and realism.

🧑‍💼 10. Stakeholders & Reviewers

• Who owns what?

• Who are the reviewers (Data, Eng, Product, Compliance)?

• Who signs off for production?

✅ Include links to related docs, dashboards, notebooks.

📋 Bonus: ML Design Doc Template

Here’s a minimal version to get started fast:

# ML Design Document

## 1. Problem Statement
...

## 2. Data Sources
...

## 3. Labels
...

## 4. Modeling Approach
...

## 5. Evaluation Metrics
...

## 6. Pipeline Overview
...

## 7. Risks & Assumptions
...

## 8. Monitoring Strategy
...

## 9. Timeline
...

## 10. Stakeholders
...

🔍 What a Great ML Design Doc Looks Like

✅ Concise (5–10 pages max)
✅ Focuses on system behavior, not architecture porn
✅ Highlights uncertainty clearly
✅ Has diagrams and tables
✅ Gets reviewed before training starts
✅ Lives somewhere searchable (Notion, Confluence, GitHub)

A great doc saves 3 months of future debugging.

🔚 Final Thoughts: Write to Think, Not to Impress

If you’re stuck writing the doc, you’re not ready to ship the model.

Design docs are not documentation. They’re thinking tools.

So treat it like an experiment:

• Write your assumptions.

• Be ready to be wrong.

• Let others challenge your logic.

• Iterate. Improve. Ship smarter.

And if you’re the lead — make writing design docs part of your ML culture.

🔮 Up Next on Gradient Descent Weekly:

• ML Experiments at Scale: The Hidden Ops Cost No One Talks About