Engineering an effective machine learning system depends on a thorough understanding of the project data set. If you have prior experience building machine learning models, you might be tempted to skip this step. After all, shouldn’t the machine learning algorithms automate the learning of the patterns from the data? However, as you are going to observe throughout this book, machine learning systems that succeed in production depend on a practitioner who understands the project data set and then applies human insights about the data in ways that modern algorithms can’t.