A feature is an input to a machine learning model. You can think of a model as a black-box function that takes features and outputs a prediction: prediction = model(features)

For example, If you're building a recommendation model at Spotify, you'll transform a stream of user listens into features like: user's top genre in last 30 days.

Featureform orchestrates the transformations on your infrastructure, manages the metadata like versioning, and allows you to serve them for training and inference.

A feature is a data point to your model. it can be as simple as the amount of a transaction (for a fraud detection model) or as complex as the avg_number_of_transactions_in_the_past_7_days_with_over_1k_in_amount_that_were_pending_review. Since you have many features, and they constantly change and evolve and being consumed by many models you need a way to store them - thats how feature stores came to be. I personalty never used.

a column in a table basically

Same as "explanatory variable" in the old (statistical) lingo.