Batch inference is an essential technique in AI, particularly when deploying ML models at scale for applications where simultaneous processing of large datasets is needed. This technique optimizes computational resources and reduces latency when generating predictions for bulk data, as opposed to making predictions one input data point at a time, known as online or real-time inference. Batch inference is widely used in scenarios where data accumulates over time and predictions need to be processed periodically, such as daily business forecasts, synthetic dataset generation, or periodic sentiment analysis of social media corpora. Additionally, it is crucial in systems demanding high throughput, efficiency, and minimal downtime, often integrated into data pipelines in big data contexts to streamline and automate prediction workflows.
The practice of batch inference likely emerged with the advent of early data processing systems in the late 20th century, although it became particularly significant with the rise of big data and the need for efficient ML model deployments in business and technology sectors around the 2010s.
While specific individuals didn't singularly define batch inference, it is a concept that evolved alongside the development of distributed computing and big data frameworks, which were spearheaded by organizations like Google and Apache Software Foundation. Key contributors in the broader context of batch processing and big data, such as those involved with tools like MapReduce and Hadoop, have indirectly influenced the techniques that underpin modern batch inference methods.