What is batch inference? The process of generating predictions from a machine learning model on a large dataset in batches, typically scheduled at regular intervals.
Batch inference, also known as offline inference, is a process where trained machine learning models process large datasets to generate predictions in batches, rather than in real time. This approach is ideal for periodic high-volume data jobs with low interactivity needs, operating on scheduled intervals and efficiently using computational resources.
Key features include high-volume data processing, scheduled execution, and scalability through distributed computing. Common applications include genomic sequencing, computational chemistry, and large-scale image processing, where vast amounts of data are analyzed periodically.
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