Why automated retraining keeps models fresh in MLOps - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to schedule automated retraining using a cron job.

MLOps

0 0 * * * [1] retrain_model.sh

Drag options to blanks, or click blank then click option'

Astart

Bbash

Cexecute

Drun

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'run' or 'execute' which are not shell commands.

Omitting the interpreter command.

The cron job runs the retrain_model.sh script using bash to automate retraining daily at midnight.

2fill in blank

medium

Complete the code to load new training data from the correct directory.

MLOps

data = load_data('[1]/new_data.csv')

Drag options to blanks, or click blank then click option'

A/data

B/old_data

C/archive

D/backup

Attempts:

3 left

💡 Hint

Common Mistakes

Using old or backup folders which do not have fresh data.

Incorrect file paths causing file not found errors.

The new training data is stored in the /data directory, so the path must point there.

3fill in blank

hard

Fix the error in the retraining function call to use the correct parameter name.

MLOps

retrain_model([1]=data)

Drag options to blanks, or click blank then click option'

Atrain_data

Bdata

Cinput_data

Ddataset

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names causing runtime errors.

Passing data without specifying the correct parameter.

The function expects the parameter train_data to receive the training dataset.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters fresh data and squares values.

MLOps

{item: item[1]2 for item in dataset if item [2] 10}

Drag options to blanks, or click blank then click option'

A**

B%

C>

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong operators causing syntax errors.

Filtering with incorrect comparison operators.

The comprehension squares items with **2 and filters items greater than 10 using >.

5fill in blank

hard

Fill all three blanks to build a dictionary of uppercase keys, values, and filter condition.

MLOps

result = { [1]: [2] for [3], v in data.items() if v > 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names causing errors.

Not applying uppercase to keys.

The keys are converted to uppercase with k.upper(), values are v, and the loop variable is k.

Practice

1. Why is automated retraining important for machine learning models?

easy

A. It makes models run faster on old data.

B. It keeps models updated with new data to maintain accuracy.

C. It reduces the size of the model files.

D. It removes the need for any human supervision forever.

Solution

1. Step 1: Understand model accuracy over time

   Models lose accuracy if they don't learn from new data as conditions change.

2. Step 2: Role of automated retraining

   Automated retraining updates the model regularly with fresh data to keep accuracy high.

3. Final Answer:

   It keeps models updated with new data to maintain accuracy. -> Option B

4. Quick Check:

   Automated retraining = model freshness [OK]

Hint: Think: new data means better model accuracy [OK]

Common Mistakes:

• Confusing speed with accuracy
• Assuming retraining reduces model size
• Believing automation removes all human roles

2. Which of the following is the correct way to schedule automated retraining using a cron job every day at midnight?

easy

A. 0 0 * * * python retrain.py

B. * * 0 0 * python retrain.py

C. 0 24 * * * python retrain.py

D. 0 0 0 * * python retrain.py

Solution

1. Step 1: Understand cron syntax

   Cron format is 'minute hour day month weekday'. '0 0 * * *' means at minute 0, hour 0 (midnight) every day.

2. Step 2: Match the correct cron expression

   0 0 * * * python retrain.py matches this format correctly to run retrain.py daily at midnight.

3. Final Answer:

   0 0 * * * python retrain.py -> Option A

4. Quick Check:

   Midnight daily cron = 0 0 * * * [OK]

Hint: Cron: minute hour day month weekday [OK]

Common Mistakes:

• Using invalid hour like 24
• Mixing up field order
• Using too many zeros

3. Given this Python snippet for automated retraining:

def retrain_model(data):
    model = load_model()
    model.train(data)
    model.save()

new_data = get_new_data()
retrain_model(new_data)
print('Retraining complete')

What will be printed after running this code?

medium

A. Retraining failed

B. Retraining complete

C. No output

D. Error: load_model not defined

Solution

1. Step 1: Trace code execution line-by-line

   After defining retrain_model, the code executes new_data = get_new_data(). get_new_data() is not defined, raising NameError.

2. Step 2: Determine printed output

   The script crashes at get_new_data() call, so no print statement is reached. The first error is about get_new_data, not load_model.

3. Final Answer:

   Error: get_new_data not defined -> Option D is incorrect because it says load_model not defined, but the actual error is get_new_data not defined. None of the options exactly match this error.

4. Quick Check:

   Undefined get_new_data() causes NameError before print [OK]

Hint: Trace for undefined functions before print statements [OK]

Common Mistakes:

• Assuming code runs to print despite undefined functions
• Expecting load_model error instead of get_new_data first
• Confusing function definition with execution

4. You set up automated retraining but notice the model accuracy is dropping after retraining. What is the most likely cause?

medium

A. The model file is missing from disk.

B. The retraining script is not scheduled to run.

C. The retraining data is outdated or irrelevant.

D. The model is too large to retrain.

Solution

1. Step 1: Understand accuracy drop reasons

   Accuracy drops if the model learns from bad or irrelevant data during retraining.

2. Step 2: Evaluate other options

   Missing model file or no retraining run would cause errors, not accuracy drop after retraining. Model size affects speed, not accuracy.

3. Final Answer:

   The retraining data is outdated or irrelevant. -> Option C

4. Quick Check:

   Bad data causes accuracy drop [OK]

Hint: Check data quality if accuracy falls after retraining [OK]

Common Mistakes:

• Confusing missing files with accuracy issues
• Assuming scheduling issues cause accuracy drop
• Blaming model size for accuracy

5. You want to automate retraining so the model updates only when new data quality passes a threshold. Which approach best achieves this?

hard

A. Add a data validation step before retraining to check quality metrics.

B. Schedule retraining to run every hour regardless of data.

C. Manually retrain the model when you feel data is good.

D. Delete old data before retraining to force fresh training.

Solution

1. Step 1: Define condition for retraining

   You want retraining only if data quality is good, so a validation step is needed.

2. Step 2: Evaluate options

   Scheduling blindly or manual retraining ignores data quality. Deleting old data may harm model learning.

3. Final Answer:

   Add a data validation step before retraining to check quality metrics. -> Option A

4. Quick Check:

   Validate data before retrain = best practice [OK]

Hint: Validate data quality before retraining [OK]

Common Mistakes:

• Ignoring data quality checks
• Relying on manual retraining
• Deleting data without reason