Feature engineering, a critical aspect of building successful machine learning models, is often overlooked due to its complexity and challenges. The process of creating high-quality features requires domain expertise, data wrangling skills, and collaboration between data scientists, engineers, and ML engineers. However, challenges such as inconsistent feature pipelines, lack of reusability, scalability issues, and technical debt hinder model deployment and performance. To address these challenges, organizations must abstract the feature pipelines, treating features as first-class citizens in the ML lifecycle. Tecton's declarative framework provides a powerful way to define features as code using Python and SQL, automatically generating infrastructure for computing and serving features. This enables collaboration between data scientists, engineers, and ML engineers, ensuring consistent and reliable features across training and production environments. By empowering teams to build and manage features as code, Tecton accelerates model development, reduces debt, and drives innovation, helping organizations unlock AI and ML potential.