Model Retraining in 2025: Why & How to Retrain ML Models?

Only ~40% ML algorithms are deployed beyond the pilot stage.¹ Such low rate of adoption can be explained with the lack of adaptation to new trends and developments such as economic circumstances, customer habits and unexpected disasters like Covid-19.

Model retraining ensures adapting the ML models that are used in business decision-making as well since the predictive accuracy of deployed models also changes and degrades as incoming data changes.

Explore what model retraining is and why you need to retrain your models:

What is model retraining?

Model retraining refers to updating a deployed machine learning model with new data. This can be done manually, or the process can be automated as part of the MLOps practices. Monitoring and automatically retraining an ML model is referred to as Continuous Training (CT) in MLOps. Model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. 

Model retraining does not change the parameters or variables used in the model. It adapts the model to the current data so that the existing parameters give more accurate and current outputs. This allows businesses to efficiently monitor and continuously retrain their models, ensuring the most accurate predictions.

Why is model retraining necessary?

As the business environment and data change, the prediction accuracy of your ML models will begin to decrease compared to their performance during testing. This is called model drift and it refers to the degradation of ML model performance over time. Retraining is required to prevent drift and to ensure that models in production provide healthy results. 

There are two main model drift types: 

• Concept drift occurs when the link between the input variables and the target variables changes over time. Since the description of what we want to predict changes, the model provides inaccurate predictions.

• Data drift happens when the characteristics of the input data change. The change in customer habits over time and the model’s inability to respond to change is an example.

What should be retrained and how?

How much data will be retrained is a critical issue. If a concept drift has occurred and the old dataset does not reflect the new environment, it is better to replace the entire dataset. This is called batch or offline learning.

Retraining a model with a new dataset can be costly and unnecessary if there’s no concept drift. With a constant stream of new data, online learning allows for continuous retraining by setting a time window to include new data and exclude old data. For example, you can periodically retrain the model with the latest dataset covering the past 12 months.

When should the models be retrained?

Depending on the business use case, the approaches for retraining a model include:

• Periodic retraining: In this approach, the model is retrained at a time interval you specify. Periodic retraining is useful when underlying data changes within measurable time intervals. However, frequent retraining can be computationally costly so determining the correct time interval is important.

• Trigger-based retraining: This method involves determining performance thresholds. Models can be retrained automatically when the model’s performance drops below this threshold (Figure 2). 

How often should you retrain a model? 

The frequency of retraining a model depends on several factors, such as the nature of the data, the task it’s performing, and how quickly the data changes. If the data evolves rapidly, such as in dynamic fields like finance or e-commerce, models might need to be retrained frequently (e.g., weekly or monthly).

For stable domains where data doesn’t change much, retraining might only be necessary every few months or annually. Monitoring model performance over time can help determine when retraining is necessary, ensuring the model remains accurate and relevant.

Model retrieving tools

Model retraining can be delivered by MLOps tools categorized as:

• Open Source developer-focused, targeting specific MLOps tasks, primarily in Python and R.
• Startups with a focus on specific MLOps tasks but are designed for non-technical users.
• Tech Giants with comprehensive and end-to-end MLOps solutions.

Check our data-driven and constantly updated list of MLOps platforms to learn more

While choosing a tool that retrain machine learning models, check out these capabilities:

1. Workflow orchestration is the ability to automate tasks and workflows in model pipelines. This capability allows businesses crate and manage directed acyclic graphs (DAGs) or flows, and retrain the model periodically.
2. Pipeline automation feature includes automated retraining process for machine learning models.
3. Scheduling refers to the automated scheduling of retraining tasks at defined intervals or based on specific triggers to trigger model retraining.
4. Custom callbacks Support for setting up custom callbacks for various stages of the workflow (e.g., success, failure, completion), which helps in minimizing human intervention.
5. Metadata tracking and logging metadata associated with model training and retraining processes, including model parameters and training dataset details. This allows ML model to adapt to the changes in data quality and data distribution based on historical data and ground truth labels.
6. Artifact management, such as new model files, datasets, and logs, which is crucial for comparing the new model with the old model.
7. Model Registry refers to a centralized repository to store, version, and manage different versions of models, ensuring the deployed model is easily accessible.
8. Continuous monitoring ensure these models meet desired performance metrics. This feature allows machine learning engineers and data scientists to track and detect model drift, target distribution and other changes in data distributions.
9. Integration with MLOps tools that provide end-to-end functionality for retraining models, supporting tasks from orchestration to monitoring and model validation.

Is fine-tuning the same as retraining?

Fine-tuning and retraining are both techniques used to improve a machine learning model, but they differ in scope and approach. Fine-tuning is a more specialized process where an already trained model is refined by making adjustments to certain parameters or training it with a smaller, more specific dataset. This allows the model to better adapt to new tasks or domains without starting from scratch.

Retraining, on the other hand, refers to the complete process of re-training the model with a more extensive and updated dataset, often to correct performance degradation or adapt to significant changes in data. Here’s a comparison table:

FAQs

Further reading

To read more about the entire ML model pipeline and MLOps lifecycle and to be informed about MLOps solutions related to your business:

• CI/CD for Machine Learning: What it is & Benefits
• ML Model Management: Challenges & Best Practices 
• Compare 45+ MLOps Tools: A comprehensive vendor benchmark

External sources

• 1. The potential of machine learning in services operations | McKinsey. McKinsey & Company
• 2. Retraining Model During Deployment: Continuous Training and Continuous Testing. neptune.ai
• 3. MLOps Principles.