In Episode 22, we covered training data—the foundation of every machine learning system.

But raw data alone isn’t enough.

In this episode of AI Made Simple, we continue our 5-part series on the machine learning lifecycle by diving into feature engineering—the step where raw data is transformed into meaningful signals that models can actually learn from.

Using a recommendation system example, we break down how user behavior gets converted into structured inputs, and why this step is often more important than the model itself.

We also cover key concepts including:

• Aggregations and time-based features
• Categorical and interaction features
• Real-time vs batch features
• Feature stores and why they matter
• Feature drift and how it impacts models

This is Part 2 of the series. Next, we’ll explore modeling and training, and how models actually learn from these features.