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  /  Latest News   /  A Beginner’s Guide on Retraining Machine Learning Models
Retraining machine learning models

A Beginner’s Guide on Retraining Machine Learning Models

Global Tech Outlook outlines a guide on retraining machine learning models

This article is for aspiring ML practitioners who need to have a strong understanding of machine learning models as well as the details on retraining machine learning models. AI algorithms, real-time training data, and machine learning are essential for training these models to work efficiently and effectively. There is also an important concept known as model drift that helps to know more about retraining machine learning models. Let’s dig deep into this beginner’s guide on retraining machine learning models.

 

Machine learning models

Machine learning models are popularly known as files that are trained to recognize objects and patterns in this tech-driven world. Companies can leverage these machine learning models to generate accurate predictions and meaningful insights from analyzing these real-time data. ML practitioners can utilize some useful properties of this blend of AI algorithms and machine learning— involvement of a repeated decision for automation and consistent result, the unexplainable theory behind the generated outcomes, and labelling structured, unstructured and semi-structured data for seeking appropriate results.

 

Model drift

Model drift is a concept of depreciation of a machine learning model’s predictive power over the years of changing environment. The future is dynamic as well as the customer’s taste and preference. Thus, there is a constant modification in the environment and large-scale data that degrades the power of a machine learning model. ML practitioners call this concept model drift. It is essential for them to detect these model drift in the early stage. Otherwise, a business has to face a serious consequence in the tech-driven market and it may affect customer engagement and high revenue.

There are two types of model drift— concept drift where machine learning models cannot map those features to the target variable and are no longer available for a new trendy environment. The other one is data drift that there are underlying changes in the data because of seasonality, consumer preference, and many more.

 

Retraining machine learning models

ML practitioners should know the appropriate timing for retraining machine learning models to avoid serious consequences in the nearby future. Retraining ML models is important because models depend on large-scale data that needs to be updated over a period of time efficiently. Drastic changes or model drift can affect the model performance from bad to worst. There are multiple hints for aspiring ML practitioners to look for the appropriate time for retraining machine learning models— metrics in the performance of models has downgraded, a gap between predictions and trained data, unusable training data, adversarial environment, highly competitive space, geographic shifts, economic factors of a country, and many more.

There are also some ways to observe the difference between training data and a model drift in performance— histogram, K-S statistic, target distribution, correlation, etc. ML practitioners should have sufficient knowledge of these concepts to observe the right time to detect model drifts for retraining machine learning models.

That being said, retraining machine learning models is a little expensive depending on any current scenario. There are three types of costs involved with this mechanism— computational cost, labor cost, as well as implementation cost. ML practitioners should focus on the right decision-making process if there is a balance between cost and benefit.