Why automated retraining keeps models fresh in MLOps - Why It Works

Introduction

Machine learning models can lose accuracy over time as new data appears. Automated retraining helps update models regularly so they stay accurate and useful without manual effort.

When your model's input data changes frequently and you want to keep predictions accurate.

When you deploy a model in production and want it to adapt to new trends automatically.

When manual retraining is too slow or error-prone for your business needs.

When you want to reduce human workload by scheduling retraining tasks.

When you want to improve model performance continuously with fresh data.

Commands

This command runs an MLflow project that trains a model with specified parameters. It starts the training process using the latest data.

Terminal

mlflow run . -P alpha=0.5 -P l1_ratio=0.1

Expected Output

2024/06/01 12:00:00 INFO mlflow.projects: === Run (ID=123abc) started ===

2024/06/01 12:00:05 INFO mlflow.projects: Training model with alpha=0.5 and l1_ratio=0.1

2024/06/01 12:00:10 INFO mlflow.projects: Model training completed

2024/06/01 12:00:10 INFO mlflow.projects: === Run (ID=123abc) succeeded ===

→

-P alpha - Set regularization strength parameter

→

-P l1_ratio - Set mix ratio between L1 and L2 regularization

This command serves the newly trained model on port 5000 so it can respond to prediction requests.

Terminal

mlflow models serve -m runs:/123abc/model -p 5000

Expected Output

2024/06/01 12:00:15 INFO mlflow.models: Serving model from runs:/123abc/model on port 5000

2024/06/01 12:00:15 INFO mlflow.models: Model server listening at http://127.0.0.1:5000

→

-m - Specify model URI to serve

→

-p - Set port number for the model server

This command creates an MLflow experiment to organize all retraining runs under one name for easy tracking.

Terminal

mlflow experiments create --experiment-name automated-retraining

Expected Output

Created experiment with ID 5

→

--experiment-name - Name the experiment for grouping runs

This command lists all retraining runs under the automated-retraining experiment to monitor progress and results.

Terminal

mlflow runs list --experiment-id 5

Expected Output

Run ID    Status    Start Time

123abc    FINISHED  2024-06-01 12:00:00

124bcd    RUNNING   2024-06-02 12:00:00

→

--experiment-id - Specify which experiment's runs to list

Key Concept

Automated retraining updates models regularly with new data so they stay accurate and useful without manual effort.

Common Mistakes

Not scheduling retraining regularly

The model becomes outdated and predictions lose accuracy over time.

Set up automated schedules or triggers to run retraining frequently.

Using stale data for retraining

Retraining on old data does not improve model freshness or performance.

Always use the latest relevant data when retraining the model.

Not tracking retraining runs

You cannot compare model versions or detect issues without tracking.

Use tools like MLflow experiments to organize and monitor retraining runs.

Summary

Run model training commands with updated data and parameters to refresh the model.

Serve the newly trained model so it can handle prediction requests.

Create and use experiments to track all retraining runs for monitoring.

List retraining runs to check status and results of automated updates.

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