Scaling Feature Engineering Pipelines with Feast and Ray

Production machine learning systems often struggle with inconsistent features and slow, sequential data processing. A feature store like Feast creates a centralized, versioned repository to manage features, ensuring consistency between training and serving and preventing data leakage. To accelerate performance, the distributed computing framework Ray parallelizes heavy workloads, such as calculating features across multiple time windows. By using Feast for structured feature management and Ray for computational power, teams can build scalable and reliable feature engineering pipelines.