Building robust machine learning (ML) systems increasingly depends on external data signals, especially those originating from the web: product prices, job postings, news articles, app reviews, social media, and more. Transforming this raw, noisy, and constantly changing web data into reliable, versioned, and discoverable ML features requires a disciplined approach that combines modern web scraping with feature store technology and data engineering best practices.