Automated ML challenges and opportunities
We have discussed the benefits of automated ML, but all these advantages are not without their fair share of challenges. Automated ML is not a silver bullet and there are several scenarios where it would not work. The following are some challenges and scenarios where automated ML may not be the best fit.
Not having enough data
The size of the dataset is a critical component for automated ML to work well. When feature engineering, hyperparameter optimization, and neural architectural search are used on small datasets, they do not yield good results. The dataset has to be significantly large for automated ML tools to do their job effectively. If this is not the case with your dataset, you might want to try the alternative approach of building models manually.
Model performance
In a small number of cases, the performance you get from out-of-the-box models may not work – you may have to hand-tune the model for performance or...
