10 Machine Learning Model Training Mistakes - AI Summary

By Sandeep Uttamchandani, Ph.D., Both a Product/Software Builder (VP of Engg) & Leader in operating enterprise-wide Data/AI initiatives (CDO) In this article, I share the ten deadly sins during ML model training -- these are the most common as well as the easiest to overlook. During model training, there are scenarios when the loss-epoch graph keeps bouncing around and does not seem to converge irrespective of the number of epochs. There is no silver bullet as there are multiple root causes to investigate -- bad training examples, missing truths, changing data distributions, too high a learning rate. The most common one I have seen is bad training examples related to a combination of anomalous data and incorrect labels. The more the same data is used for parameter and hyperparameter settings, the lesser confidence that the results will actually generalize.