What is Data Drift Monitoring & How it’s Useful? 

The basic assumption in developing any machine learning model is based on the data you use to train the machine and mimic the real-world data. But, how can you ensure that the model will continue to function as you trained (it) after it is deployed to production? You might need some ML model monitoring tools to ensure that the models in production are not having any issues. 

There have been several cases of malfunction of models, and the main reason was the data drift. Manufacturers face this challenge a lot, and to minimise or eliminate the data drift, they need to use different tools. 

But, what is data drift? 

It doesn’t matter how well you train a model because, even after supervised learning, when you deploy the model in production and don’t mimic the data used in training, you have a problem here. Why does this happen? What can you do to prevent it? 

The inability of the model to mimic the trained skills or movements is because of the data drift. Data-drift can be described as a variation in the production data from the data you used to test and approve a model before the actual production. You will find many factors that cause data drift, and one of the main factors is the time dimension. 

The change in time dimension can be explained by the diagram given below: 

Problem understanding 


Data collection & labelling 


Data cleanup 


Model training 



What you see above are the standard ML model development stages. 

What happens when you don’t identify the data drift in time? 

When this happens, the predictions will go wrong. For example, if Netflix shows the wrong recommendation, it will affect the views and the corporate’s interests. Suggesting a suitable series or movie is the only way to keep the viewers engaged. One can use the ML model monitoring tools to check these issues and then retrain the AI/programme. 

How can you track down these drifts? 

Data drifts are identified using sequential analysis, model-based, and time distribution-based methods. There sequential analysis methods like DDM (Drift Direction Method)/EDDM (early DDM) rely on the error rate to get the drift detection. A model-based method uses a custom model to recognise the drift time-distribution-based methods. 

How to configure drift detection in Azure ML? 

Azure ML (machine learning) is a cloud service by which you can speed up and manage the learning project lifecycle. This service is utilised by engineers, data scientists, and ML professionals to train and deploy models. They use it in their daily working hours, and it helps them manage MLOps (Machine Learning Model Operationalization Management). 

Microsoft offers an automated path to recognise data drift incorporated into the Azure ML workspace. This feature is presently in public preview. This feature utilises satistical procedures to recognise drift using various time windows—making it easier for them to calculate drift for chosen features. 

What are the steps involved in Azure Machine Learning? 

There are four main steps involved in implementing data drift in Azure ML: 

  • Register the baseline dataset 
  • Develop a data drift detector 
  • Select the feature 
  • Run 

With these steps, you can easily detect the data drift in the models. Drift detection and model monitoring are crucial parts of the ML Model lifecycle, and you need to enhance it frequently to deploy models with higher efficiency successfully. Only detecting these the drift and handling the issue with better strategy will result in better performance of the models. 

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