Common Pitfalls in Machine Learning
Picture this: a seasoned data science manager just launched a new recommendation engine to boost product sales. The model performed brilliantly in tests, but now, customer interest is lukewarm. The problem? The model had gotten too good at mirroring the training data – niche tastes of early adopters that didn’t reflect broader customer preferences.
Machine learning (ML) promises incredible things, but it’s dangerously easy to stumble. According to a survey of over 500 developers working with ML systems (https://www.civo.com/newsroom/ai-project-failure), more than half (53%) of respondents have abandoned between 1% and 25% of ML projects, with an additional 24% having left between 26% and 50% of projects. Only 11% of developers said they have never abandoned a project. The first lesson is this: ML isn’t some magic algorithm that just needs data. It’s about understanding what kind of model is right for the job...
