What is Model retraining?
ML model retraining is adapting a previously trained model to a new task or improving its performance on an existing task by using a different dataset.
To retrain a model, take a model that has already been trained and modify its parameters by training it on a fresh dataset. This is achieved by “few-shot learning”, where the model is trained with just a limited quantity of labeled data. Once the model has been retrained, it may be used to either take on the new duty or enhance its performance on the original.
- Retraining models may be a beneficial practice because it can save time and computing resources compared to training a model from scratch. IT can also improve performance.
Why and when should you retrain your model?
Before you retrain a machine learning model, you need to have a good understanding of your company’s use cases. When and how frequently you need to update your model is crucial in certain scenarios. Algorithms used in commercial applications need continuous retraining. In the same way that ML models trained on behavioral data need more frequent retraining than those learned on manufacturing data due to the dynamic nature of the former, the latter requires less frequent retraining.
An incentive for good performance
You need to find out where you started with your metrics after putting your model into production. This method relies on the model’s declining performance in production as an indication for a rebuild. If your model’s accuracy drops below the threshold you’ve established using the truth, the retraining process will begin immediately. This method anticipates the presence of an advanced monitoring system in production.
The delay in obtaining the ground truth is a disadvantage depending on the model’s performance in production. It might take anywhere from 30 to 90 days to get the whole story on a loan or credit model. You will have to wait to begin an automated model retraining position until you get your results, which may have already had an effect on the company.
This method excels in situations when the bare facts may be ascertained in a short amount of time. Predictions made by models may be tracked in real time.
Initiate actions when data changes
Upstream data in production may be monitored for shifts in distribution. From there, you could infer that your model needs updating or that you are in a very dynamic setting. When your model in production doesn’t provide you with immediate feedback or ground truth, this strategy is a solid option to examine.
The performance-based trigger may be used in tandem with this method. When your model is put into production, it may see a drop in performance due to data drift, which might cause it to fail to meet the minimum acceptable performance level. For model retraining machine learning, this will immediately initiate a build.
Anytime, Anywhere Retraining
This heuristic method of retraining your models is done manually and often uses conventional methods. This method is used by the vast majority of startups and it has the potential to boost model performance, but is ultimately not the best option. It’s important to automate your machine learning processes in a commercial setting.
Interval Retraining: A New Way to Learn
Do you need to know how often you should retrain your model? Regular and continuous machine learning training is the simplest and most natural method. You may predict when your model’s retraining pipeline will be activated by selecting a retraining interval. How often your training data is refreshed will determine how accurate your predictions will be.
Only if it makes sense for your business use case should you retrain your model based on an interval. Otherwise, picking a time window at random may add unnecessary complications and may even result in a less accurate prediction than before.
Significance of Model retraining
Continuously training a machine learning model is the key to optimal performance.
- Improve the model’s performance. Update the training data and regularly retrain the model to boost the it’s accuracy and efficiency. Improved F1 scores, precision, recall, and accuracy are all possible outcomes.
- Reduce model bias. Adding fresh data that more accurately represents the variety of the actual world and keeping track of the model’s performance on various groups throughout continuous training might mitigate model bias.
- Cost-effective. Continuous training may save time and money compared to starting again with a freshly retrained model. You can conserve time and computing power by starting with a pre-trained model.
- Adaptability. The model’s performance may decrease over time if it is not updated to account for the changing nature of the data and the world beyond the lab. The model may be trained continuously to account for these variations and preserve its performance.
In conclusion, training your models continuously helps keep them up-to-date, improves performance, reduces bias, strengthens resilience, and saves costs.