Model Retraining

What does Model Retraining Mean?

Model retraining in machine learning (ML) is a process of tweaking an already developed algorithm for a new mission or bolstering its performance for an existing mission through the utilization of another dataset. To regenerate a model, we use an existing trained prototype and adjust its parameters by introducing it to a new dataset.

This process involves "few-shot learning" where the model undergoes training with a minimum amount of labeled data. Once the model gets retrained, it can undertake a new course or improve its productivity at the previous task. Model retraining can be a cost-effective practice because it conserves time and computational resources needed for training a completely new model, and it can also potentially enhance its performance.

When and Why to Retrain your Model?

Before embarking on retraining your ML model, comprehending your business use case is crucial. Getting to know when and how regularly your model needs an upgrade is essential in some conditions. Algorithms implemented in business applications need incessant retraining. Similarly, ML models trained on behavioural data demand more frequent retraining than those trained on manufacturing data due to their volatile nature.

Performance-Based Incentives

Understanding your initial metrics after deploying your model is important. This approach uses the degrading performance of the model in production as a sign to need a rebuild. When your model’s preciseness drops under your established threshold according to reality, retraining should begin immediately. This approach expects an advanced monitoring system in production. However, relying on the production performance model has its downside as it may take 30 to 90 days to get a complete picture, leading to a delay in beginning an automatic model retraining process.

Responses to Data Alterations

By monitoring upstream data in production for distribution changes, you can deduce if your model requires an upgrade. This method can be combined with the performance-based trigger. When data drift causes a drop in the model's efficiency, it may fall short of the minimum acceptable performance level, thus triggering an instant build for model retraining in ML.

Frequent and Manual Retraining

This manual method is typically employed by startups for most retraining. Although it has the potential to enhance model performance, it's not the best choice due to a lack of automation.

Interval Retraining

Choosing a retraining period can help predict when your model's retraining pipeline will be activated. However, choosing a random retraining interval may bring unnecessary complications and could result in less accurate predictions.

Importance of Model Retraining

Retraining a machine learning model continuously is critical for maintaining optimal performance. Ensuring regular updates and retraining of your model with fresh data will enhance its precision and efficiency. Also, feeding the model with varied, real-world data can help reduce model bias. Continuous training can be cost-effective and allows for adaptability to changes in data over time. By keeping models up-to-date, performance and efficiency are improved, bias is reduced, resilience is enhanced, and costs are reduced.

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