What are the most common reasons for an ML model to fail in production?
Machine learning (ML) models are powerful tools for solving complex problems and creating value for businesses and customers. However, developing a successful ML model is not enough to ensure its performance and reliability in production. Many factors can cause an ML model to fail or degrade over time, leading to poor user experience, lost revenue, or even ethical and legal issues. In this article, you will learn about some of the most common reasons for an ML model to fail in production, and how to avoid or mitigate them.
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Implement robust data cleaning:Ensuring your data is clean is like spring cleaning for your models. It means scrubbing away the irrelevant or incorrect bits that could lead your machine learning predictions astray.
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Validate and monitor data:Think of this as a quality check on your model's diet. Regularly confirming that the incoming data is nutritious for your algorithms keeps them healthy and accurate in their predictions.