Run Your AI Model as Close as Possible to Your Data

The new RedisAI module for serving tensors and executing deep learning models enables colocality, allowing models to run where the data lives, reducing costs and improving performance. It introduces a new data type called Tensor, Models, and Scripts, which inherit enterprise-grade features of Redis, making it easy to scale model serving and reduce infrastructure costs. With integrated backends such as TensorFlow and PyTorch, RedisAI decouples backend choice from application services, allowing for flexibility and ease of use. The module also includes features like automatic batching, DAG commands, and metrics analysis, which will be added in the near future, enabling users to optimize model performance and efficiency.

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