Feature engineering is one of the most important steps in machine learning - it's how you improve the predictive performance of your models. In a corporate setting, one underappreciated problem is that you don't want to reinvent your feature engineering work in every project. To solve this, you need a way to reuse features from one project to the next.
In this live training, Colin teaches you how to manage the features for your machine learning models, to save you time and improve the consistency of your models.
Colin PriestChief Evangelist at FeatureByte
As Chief Evangelist at FeatureByte, Colin helps organizations manage feature engineering processes. He is an expert in digital transformation, data strategy, and automating data processes. He was previously Global Lead for AI Governance at DataRobot and Chief Technology Officer at IntelliM.