Continuous training is a feature of machine learning operations in which machine learning models are dynamically and continuously retrained to respond to changes in the data before being redeployed.
Once you have deployed your machine learning model into production, differences in real world data will result in model drift. So retraining and redeployment will likely be required. In other words, retraining and redeployment should be treated as a continuous process.
Model Drift is the degradation of a model's predictive ability over time as a result of a change in the environment that contradicts the model's predictions. The term "model drift" is a bit misleading because what is changing is the environment in which the model is functioning, not the model itself. As a result, notion drift may be a more appropriate name, but both names represent the same process. This concept of model drift encompasses a number of factors that can shift.
1. Machine learning models become stale over time: As soon as your machine learning model is deployed in production, its performance deteriorates. This is due to the fact that your model is responsive to variations in the actual world, and user behavior evolves with time. Even though all machine learning models degrade with time, the rate at which they degrade varies. Data drift, concept drift, or both are the most common causes.
2. The retraining pipeline allows developers to ensure that intellectually defensible, quantitative measurements, and explainability tests are set up, as the pipeline must run reliably without human intervention. This increases the model's internal and external credibility.
Periodic training: By deciding on a retraining interval for your model, you may anticipate when your retraining process will be activated. It depends on how often you refresh your training data.
Performance-based trigger: In this approach, a rebuild is triggered because the model's performance has deteriorated in production. If your model's performance falls below a certain threshold, the retraining pipeline is automatically triggered.
Trigger based on Data Changes: You can detect changes in the distribution of your data by observing your source data in production. This could mean that your model is out of date or that you're working in a fast-paced setting.
Retraining on demand: This is a manual method of retraining your models, and it typically uses classic procedures. This method may help your model perform better, but it isn't the best.
Fixed window size: This is a simple method for selecting training data, and it's something to think about if your data is too big to fit.
Dynamic window: This method determines how much statistical data should be utilized to retrain your model by looping through several window sizes to find the best one.
Selection of representative subsamples: This method employs data points that are similar to the production data. To do so, you must first conduct a thorough review of your performance data and eliminate data that indicates the presence of drift.
Continual learning vs. Transfer learning: Continual learning, often known as lifelong learning, is a type of machine learning that attempts to replicate human learning. Transfer learning is a machine learning method that retrains a new model using an old model as a foundation.
Offline(batch) vs. Online(Incremental): Using offline learning to retrain your model entails starting over with new data. You retrain the algorithm continuously by providing data instances progressively via online learning.
Data is used by machine learning models to "understand" an issue and provide the required result. We'd like to believe that retraining a model would address all of your model's performance issues. However, this isn't always the case; things can go wrong and false alarms might be triggered. Because tracking is broken, for instance, the data distribution may shift. It's possible that new feature values arise because another team altered the price format without informing you. In some circumstances, retraining may not be the best solution, but appropriate monitoring will enable you to identify the problem quickly.
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