Robust and Scalable Data Infrastructure for Reliable AI Model Deployment

MotivationA bottleneck.Modern AI systems have a big difference in speed between memory and disk access (about 100ns vs. about 10ms).This can slow down training and inference.Objective.Create a data infrastructure that lets you access data quickly and reliably for both training and serving.How to do it.To achieve this, we implemented and tested a modular four-layer storage stack: Storage → Buffer → Record → Index.Let's take a closer look at the main results.Repetitive workloads (such as training) can achieve a cache hit ratio of up to 28.87% with 100 buffer frames.When you access something multiple times, the hit ratio is 3.2 times higher than when you access it randomly (15.77% vs. 4.93%).B+ Tree indexing allows for efficient lookup of items in O(log n) time and is expected to need 50,000 times fewer comparisons than a full scan of 1 million records.