Model retraining is crucial for keeping machine learning models accurate as data and patterns evolve. Essentially, it means updating a model by retraining it with new data to handle issues like data drift and concept drift. This ensures that the model remains relevant and performs well over time. Various retraining techniques, such as scheduled updates, performance-based triggers, online learning, and domain adaptation, help the model stay in tune with current trends and data changes.
What is Model Retraining?
Model retraining is about creating a new version of a machine learning model by reapplying the training process with updated data. As time passes, the data and relationships a model relies on can change. Without retraining, the model’s performance can drop due to two main factors:
Data Drift: Changes in the statistical properties of incoming data.Concept Drift: Changes in the underlying relationships or mappings.
Retraining helps combat these effects by incorporating new data, ensuring that models remain accurate and relevant.
Model Retraining Techniques
Maintaining the performance and relevance of machine learning models over time requires effective retraining strategies. Here are some common techniques:
Scheduled Retraining — This is training a model at regular intervals to keep it updated with new data and changing patterns.
Periodic Retraining: Regularly retraining the model on a fixed schedule, like daily, weekly, or monthly, based on the rate of new data generation.
Batch Retraining: Using a batch of new data collected over a certain period to retrain the model.Performance-Based Retraining — This is training a model when its performance drops below a certain threshold, ensuring it remains effective without unnecessary retraining.
Threshold-Based Retraining: Triggering retraining when the model’s performance drops below a certain threshold on validation data or in production.
Performance Monitoring: Continuously monitoring performance metrics (e.g., accuracy, F1 score) and initiating retraining when significant performance degradation is detected.Online Learning — This is continuously updating the model with new data as it becomes available, suitable for environments where data arrives in a stream.
Incremental Learning: Continuously updating the model with new data points as they arrive, allowing it to adapt to new patterns without full retraining.
Streaming Data: Handling data in real-time and updating the model on-the-fly, useful for time-sensitive applications.Active Learning — The model selects the most informative data points for learning, reducing the amount of labelled data needed while improving performance.
Uncertainty Sampling: Selecting the most uncertain data points for the current model and labelling them for retraining.
Diversity Sampling: Choosing a diverse set of data points that represent different areas of the feature space for retraining.Domain Adaptation — This adapts a model trained on a source domain to perform well on a target domain with different but related data.
Transfer Learning: Adapting a pre-trained model to a new, but related dataset to improve performance on specific tasks.
Fine-Tuning: Slightly modifying the pre-trained model by retraining it on a smaller, task-specific dataset.Ensemble Methods — This combines predictions from multiple models to improve overall performance, making results more robust and accurate.
Model Ensembling: Combining predictions from multiple models to improve performance and robustness. Retraining can involve updating individual models or the ensemble strategy.
Stacking and Blending: Using different models as base learners and combining their predictions in a meta-learner, retraining components as needed.CI/CD for ML — This automates the training, testing, and deployment of models, applying software engineering best practices to machine learning for reliability and efficiency.
Automated Retraining Pipelines: Setting up CI/CD pipelines that automate data ingestion, model training, validation, and deployment.
A/B Testing: Deploying new model versions in parallel with the current model to compare performance and ensure improvements before full-scale deployment.Data-Centric Techniques — This focuses on improving the quality and quantity of training data to enhance model performance without changing the model itself.
Data Augmentation: Generating new training examples through transformations (e.g., rotations, scaling) to improve model robustness.
Data Quality Improvement: Continuously curating and cleaning the training data to ensure high-quality input for retraining.Specialised Techniques — This tailors methods for specific problems, like transfer learning or federated learning, addressing unique challenges in certain applications.
LoRA (Low-Rank Adaptation): Adapting a pre-trained model to a new task by focusing on a low-rank representation, which can be computationally efficient.
Spectrum Analysis: Using spectral methods to analyse and adapt the training process based on the frequency components of the data.
Model retraining is essential for maintaining predictive accuracy in machine learning models. It ensures that models remain up-to-date with the latest data and adapt to changes in the environment. By implementing a robust and automated retraining process, businesses can avoid performance degradation, maintain customer trust, and increase revenue. If you need expert guidance in your organisation, contact our specialists at specialists@edenai.co.za or visit our website https://edenai.co.za/get-in-touch/.
This post was enhanced using information from:
Iguazio What is Machine Learning Model Retraining?
https://www.iguazio.com/glossary/model-retraining/
Dilmegani, C. (2024) Model Retraining: Why & How to Retrain ML Models?
https://research.aimultiple.com/model-retraining/
Sampathkumarbasa (2023) Mastering Model Retraining in MLOps
https://medium.com/@sampathbasa/mastering-model-retraining-in-mlops-5cc8db324666
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