What are the common mistakes made in building ML products? The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. Many companies wanted to use ML and had built up ‘smart software’ strategies but didn’t have any data. You cannot use machine learning if you have no data. You can apply ML on small data sets too, but you have to be very careful so that the model is not affected by outliers and that you are not relying on overcomplicated models.