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Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

Uncertainty, Calibration and Abstention Thresholds

Quiz: Uncertainty, Calibration and Abstention Thresholds

Explainability Methods and Their Honest Limits

Quiz: Explainability Methods and Their Honest Limits

Validation by Someone Who Did Not Build the Model

Quiz: Validation by Someone Who Did Not Build the Model

Reproducibility: Seeds, Environments and Experiment Records

Quiz: Reproducibility: Seeds, Environments and Experiment Records

Writing the Model Card and the Technical File

Quiz: Writing the Model Card and the Technical File

Handover to Monitoring: What Production Needs From You

Quiz: Handover to Monitoring: What Production Needs From You

Final exam: Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

2 of 2

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Quiz: Reproducibility: Seeds, Environments and Experiment Records

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Reproducibility: Seeds, Environments and Experiment Records
Quiz: Reproducibility: Seeds, Environments and Experiment Records

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Question 1 of 5

1. Question

What does reproducibility mean?

Using the same dataset twice
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Someone else can rebuild the model and get the same result
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Testing on multiple datasets
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Achieving high accuracy
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Question 2 of 5

2. Question

Why should you fix random seeds?

To make training faster
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To ensure the model always produces the same result given the same data
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To reduce memory usage
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To improve accuracy
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Question 3 of 5

3. Question

What is an environment specification?

The country where the model runs
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Documentation of programming language, library versions and operating system
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The deployment setup
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The data storage location
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Question 4 of 5

4. Question

Should you delete experiment records where the model performed poorly?

Yes, to keep records clean
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No, poor results are important information and should be documented
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Yes, if they are statistically insignificant
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Yes, to reduce storage
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Question 5 of 5

5. Question

What should an experiment record include?

Only the best hyperparameters
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Hyperparameters, performance, date, time, person who ran it and code version
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Only final model performance
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Only successful experiments
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