Managing Machine Learning Projects

7 Making useful models with ML

This chapter covers:

• Transforming data for processing
• Injecting information with feature engineering
• Designing the model’s structure
• Running the model development process
• Deciding which models to retain and which to reject

Sprint 2 is the rollercoaster ride that we’ve been working toward; finally, we’re going to do some ML! The success or failure of this phase of the project is the pivot point for everything else. Although we created the conditions for success with the work in presales, sprint 0, and sprint 1, all this work will be for nothing if we can’t implement useful models. Creating a model is easy if you’ve done the hard part of getting and preparing the data. A simple model can involve writing a single ...