This post discusses the benefits of using Redis for real-time predictive engines in machine learning applications. It highlights that most ML toolkits focus on learning, leaving the problem of building a predictive engine to developers. Redis fills this gap by providing a familiar and full-featured data store for building applications. The advantages include simple and powerful data management, reduced operational complexity, and fast throughput due to in-memory data storage. Additionally, Redis already integrates with most companies' tech stacks, making it easier to manage and scale compared to custom services.