Top 10 Mistakes in a Machine Learning Project

Machine Learning has become more accessible than ever. With tools like Qlik Predict and other AutoML platforms, building a model no longer requires writing code or tuning algorithms manually.

But accessibility does not mean simplicity.

In this video, Igor Alcantara walks through the Top 10 Mistakes in a Machine Learning project, especially in AutoML environments. These are the errors that quietly destroy performance long before an algorithm fails. From vague business questions and poor target definitions to data leakage, rare event blindness, bias, drift, and the black box trap, we explore why many models look great in training and struggle in production.

Watch the full video here:

https://youtu.be/17CvwpxJw1c