Model documentation and model cards in MLOps - Step-by-Step Execution

Process Flow - Model documentation and model cards

Start Model Training

↓

Collect Model Info

↓

Create Model Card

↓

Add Documentation Details

↓

Review & Approve

↓

Publish Model Card

↓

Use Model with Info

This flow shows how after training a model, we gather info, create a model card with documentation, review it, then publish for users.

Execution Sample

MLOps

model_info = {
  'name': 'ImageClassifier',
  'version': '1.0',
  'metrics': {'accuracy': 0.92},
  'limitations': 'May misclassify rare images'
}
create_model_card(model_info)

This code collects key model details and creates a model card summarizing the model's info and limitations.

Process Table

Step	Action	Input/Condition	Output/Result
1	Train model	Training data ready	Model trained with weights
2	Collect info	Model trained	Gather name, version, metrics, limitations
3	Create model card	Model info collected	Model card draft created
4	Add documentation	Draft model card	Add usage, ethical notes, data info
5	Review & approve	Completed draft	Model card approved
6	Publish model card	Approved card	Model card published for users
7	Use model	Model card available	Users access model with clear info

💡 Model card published and ready for user reference

Status Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
model_info	{}	{name, version, metrics, limitations}	Model card draft with model_info	Model card with added docs	Approved and published model card

Key Moments - 3 Insights

Why do we include limitations in the model card?

Including limitations helps users understand where the model might fail, as shown in step 2 and 4 where limitations are collected and documented.

What happens if the model card is not reviewed before publishing?

Skipping review (step 5) risks publishing incomplete or incorrect info, which can confuse users or cause misuse.

Is the model card created before or after training the model?

The model card is created after training (step 3), because it needs info from the trained model.

Visual Quiz - 3 Questions

Look at the execution table, what is the output after step 3?

A Model card draft created

B Model trained with weights

C Model card published

D Model info collected

💡 Hint

Check the 'Output/Result' column for step 3 in the execution table.

At which step is the model card approved?

A Step 2

B Step 5

C Step 4

D Step 6

💡 Hint

Look for 'Review & approve' action in the execution table.

If limitations were not added, which step would be affected?

A Step 1

B Step 6

C Step 4

D Step 7

💡 Hint

Step 4 is where documentation details like limitations are added.

Concept Snapshot

Model documentation and model cards:
- Created after model training
- Summarize model name, version, metrics, limitations
- Include usage and ethical notes
- Reviewed before publishing
- Help users understand model capabilities and risks

Full Transcript

Model documentation and model cards help explain what a machine learning model does, how well it performs, and its limits. After training a model, we collect key info like name, version, accuracy, and limitations. This info is used to create a model card draft. Then, we add more documentation details such as usage instructions and ethical considerations. The draft is reviewed and approved to ensure accuracy. Finally, the model card is published so users can access clear, trustworthy information about the model before using it. This process helps prevent misuse and builds user confidence.