Model documentation and model cards in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a model card in machine learning?

A model card is a short document that explains key details about a machine learning model, like its purpose, performance, and limitations. It helps users understand how and when to use the model safely.

beginner

Why is model documentation important?

Model documentation helps teams keep track of how a model was built, tested, and should be used. It improves transparency, trust, and helps avoid mistakes when deploying or updating models.

intermediate

Name three key sections typically found in a model card.

1. Model details (name, version, developer) 2. Intended use and limitations 3. Performance metrics on different data groups

intermediate

What kind of information does model documentation usually include?

It includes data sources, training methods, evaluation results, ethical considerations, and instructions for deployment and maintenance.

intermediate

How do model cards help with ethical AI practices?

Model cards disclose biases, fairness issues, and usage warnings. This helps users avoid harmful or unfair outcomes and promotes responsible AI use.

What is the main purpose of a model card?

A To train the model automatically

B To store the model's code

C To explain a model's purpose, performance, and limitations

D To deploy the model to production

Model cards provide clear information about the model's intended use and performance.

Which of these is NOT typically included in model documentation?

A Training data description

B User personal data

C Model evaluation results

D Deployment instructions

User personal data should not be included in model documentation for privacy reasons.

How do model cards support ethical AI?

A By hiding model limitations

B By encrypting the model

C By increasing model speed

D By disclosing biases and usage warnings

Model cards help users understand ethical risks by sharing bias and fairness information.

Which section would you find in a model card?

A Intended use and limitations

B Model's favorite color

C Developer's personal biography

D Hardware specifications

Intended use and limitations help users know when and how to apply the model.

What is a benefit of good model documentation?

A It improves transparency and trust

B It automatically fixes bugs

C It reduces the model size

D It makes the model run faster

Clear documentation helps teams and users trust and understand the model.

Explain what a model card is and why it is useful.

Think about how a simple document helps explain a model.

List key information that should be included in model documentation.

Consider what someone new to the model needs to know.

Practice

1. What is the main purpose of a model card in MLOps?

easy

A. To store the model's training data

B. To explain what a model does and how to use it safely

C. To deploy the model to production

D. To monitor the model's runtime performance

Solution

1. Step 1: Understand the role of model cards

   Model cards provide clear information about a model's purpose, performance, and safe use.

2. Step 2: Differentiate from other MLOps tasks

   Storing data, deployment, and monitoring are separate tasks not covered by model cards.

3. Final Answer:

   To explain what a model does and how to use it safely -> Option B

4. Quick Check:

   Model card purpose = Explain model use safely [OK]

Hint: Model cards describe model use and safety, not deployment [OK]

Common Mistakes:

• Confusing model cards with deployment tools
• Thinking model cards store training data
• Assuming model cards monitor runtime

2. Which section is NOT typically included in a model card?

easy

A. Model performance metrics

B. Ethical considerations and limitations

C. Source code for model training

D. Intended use and users

Solution

1. Step 1: Identify typical model card contents

   Model cards usually include performance, ethics, limitations, and intended use.

2. Step 2: Recognize what is excluded

   Source code is not part of the model card; it belongs in code repositories.

3. Final Answer:

   Source code for model training -> Option C

4. Quick Check:

   Model card excludes source code [OK]

Hint: Model cards describe, not contain source code [OK]

Common Mistakes:

• Including source code in model cards
• Confusing documentation with code repositories
• Ignoring ethical sections

3. Given this snippet from a model card:

"performance": {"accuracy": 0.92, "f1_score": 0.89},
"limitations": "Not tested on non-English data",
"ethical_considerations": "May reflect training data bias"

What does this information tell you about the model?

medium

A. The model is only for English data and has perfect fairness

B. The model has no ethical concerns and works on all languages

C. The model's accuracy is below 50%, so it is unreliable

D. The model is highly accurate but may not work well on non-English data

Solution

1. Step 1: Analyze performance metrics

   Accuracy 0.92 and F1 score 0.89 indicate good performance.

2. Step 2: Review limitations and ethics

   Limitations mention lack of testing on non-English data; ethics warn about bias.

3. Final Answer:

   The model is highly accurate but may not work well on non-English data -> Option D

4. Quick Check:

   Performance + limits = Accurate but language-limited [OK]

Hint: Check performance numbers and limitations for model scope [OK]

Common Mistakes:

• Ignoring limitations about language
• Assuming no ethical issues from bias note
• Misreading accuracy as low

4. You find a model card missing the "ethical considerations" section. What is the best way to fix this?

medium

A. Add a section describing potential biases and fairness issues

B. Remove the model card entirely since it is incomplete

C. Ignore it because ethics are not important for model cards

D. Replace it with just performance metrics

Solution

1. Step 1: Identify missing ethical info

   Ethical considerations help users understand risks and biases.

2. Step 2: Add relevant ethical details

   Include potential biases, fairness, and impact to complete the card.

3. Final Answer:

   Add a section describing potential biases and fairness issues -> Option A

4. Quick Check:

   Ethics section needed = Add bias/fairness info [OK]

Hint: Always include ethics to build trust and transparency [OK]

Common Mistakes:

• Deleting incomplete model cards
• Ignoring ethical importance
• Replacing ethics with only metrics

5. You want to create a model card for a new image classification model. Which combination of information should you include to ensure clear communication and trust?

hard

A. Purpose, performance metrics, limitations, ethical considerations, and intended users

B. Only the model's accuracy and training dataset size

C. The source code and deployment scripts

D. The hardware specifications used for training

Solution

1. Step 1: Identify key model card components

   Include purpose, performance, limits, ethics, and intended users for clarity.

2. Step 2: Exclude unrelated details

   Source code, deployment scripts, and hardware specs belong elsewhere.

3. Final Answer:

   Purpose, performance metrics, limitations, ethical considerations, and intended users -> Option A

4. Quick Check:

   Complete model card info = Purpose + performance + ethics + limits [OK]

Hint: Include purpose, performance, limits, ethics, users for trust [OK]

Common Mistakes:

• Including code or hardware in model cards
• Providing only metrics without context
• Ignoring ethical and user info