How AI Inference Works

At its essence, AI is the process of converting raw data into actionable insights. The typical AI workflow occurs in three stages: data engineering, AI training, and inference. Each stage has different memory, compute, and latency requirements.

Data engineering has high memory requirements, so large datasets can be efficiently preprocessed to shorten the time required to sort, filter, label, and transform the data.

AI training is usually the most computationally intense stage of the workflow. Based on the size of the dataset, this process can take several hours or even days to complete.

The inference stage has stringent latency requirements, often requiring milliseconds or faster processing speeds.