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 ===

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-P alpha - Set regularization strength parameter

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-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

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-m - Specify model URI to serve

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-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

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--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

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--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.