 Machine learning was once the domain of specialized researchers, with complex models and proprietary code required to build a solution. But, Cloud AutoML has made machine learning more accessible than ever before. By automating the model building process, users can create highly performant models with minimal machine learning expertise (and time). However, many AutoML tutorials and how-to guides assume that a well-curated dataset is already in place. In reality, though, the steps required to pre-process the data and perform feature engineering can be just as complicated as building the model. The goal of this post is to show you how to connect all the dots, starting with real-world raw data and ending with a trained model. |