I think it’s a bit of an overstatement to call this an end-to-end solution.<p>What they are starting with here is a single table of data with all the features already defined and an existing binary label column. Typically when this type of data is collected in the field it is much more fine grained (i.e many observations collected over time) and unlabeled (e.g how do we define a true example? How many false examples do we select?).<p>The competition description even goes as far to say “We have chosen a dataset that you can get started with easily”.<p>So, yes, this is a cool demonstration of Google's product, but the success in the competition might not extend to the problems real business face when trying to apply ML to a problem like this.<p>That being said, I do think AutoML can help with these problems as it is extended to handle data that isn’t in a single table already.<p>For example, I’m a developer of a open source library called Featuretools (<a href="https://github.com/Featuretools/featuretools" rel="nofollow">https://github.com/Featuretools/featuretools</a>) that tries to automate feature engineering for temporal and relational datasets. Basically, it helps data scientists prepare real world data into the form this competition starts with.