COVID-19 and How Concept Drift Affects Data Analysis


COVID-19 and How Concept Drift Affects Data Analysis Team
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Machine learning works by building ML models using data. The data you use has a dramatic effect on whether your ML models will be accurate or not. If your model isn’t correct, you might have what is called concept drift. Concept drift means that the input has changed so much that your ML model cannot accurately make predictions based on it anymore. An example of this is a model made with COVID-19 data.

As things have progressed, the data coming in has made it difficult for machine learning models to keep up. This is an important concept in data science, which is why companies need to understand how it works. Many of the underlying behaviors we used to determine what will happen next have certainly changed in this new era. The field of data science will need to compensate.

The Effect of Concept Drift on Business

As mentioned above, human behavior has dramatically shifted in the age of the pandemic, meaning that more ML models will become inaccurate. Since the pandemic still isn’t over, you will need to have MLOps as a way of alleviating the problems that come from Concept drift. With the way things are looking, it is going to be a major issue for businesses.

The foundation of every business is being able to accurately predict how customers will react in certain situations. This is becoming very difficult because of how fundamentally transformed our world has been. That transformation is having a massive effect on the way we look at business, and that is something to think about. Predictive models that used to work will stop as the underlying data changes.

MLOps Helps with Concept Drift

No one could ever have foreseen how the pandemic would change data science and machine learning. Professionals used to think they could use past behavior to accurately predict how things would be in the future. However, the pandemic has caused such a dramatic departure from the way we do things that it might not be possible in the future.

That massive change has made MLOps more important than ever. This is because MLOps makes it easy to improve your ML models when Concept drift occurs. Instead of spending a lot of time rebuilding your project or even abandoning machine learning altogether, it provides you with a system that makes it possible to build change into each model you build. You can easily iterate, making it possible to go with the flow when the underlying data changes. That is the primary way businesses will have to adjust to the new landscape.

Adapt or Die

As a business, the pandemic has put you in a tough spot. However, you will have to adapt to survive. Your ML models won’t fix themselves, which is why MLOps is vital. It needs to be a core part of the way you do business. Things will move quickly, and that necessitates being able to change your ML models on the fly. Your data science operation will need to monitor for concept drift, which will ensure that your ML models continue to provide your business with value.

About the Author Team Enterprise AI/ML Application Lifecycle Management Platform

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