Batch Inference


Batch inferencing is the process of running a set of data through an AI model all at once. It’s like feeding the model a whole tray of questions and getting a tray of answers back, instead of asking and answering one by one.

Batch inferencing and online inference represent two extremes in the world of machine learning. At one end, there are scenarios where delayed inferencing, also known as batch inferencing, is the only viable option. Take weather prediction models, for instance. These models operate slowly and require extended periods for simulation, making real-time processing unrealistic. On the opposite end of the spectrum is online, or real-time, inference. This is exemplified by applications like chatbots, which must operate in real-time. A chatbot that takes more than a few seconds to respond would not be effective.

Real World Use Cases

Healthcare Diagnostics from Medical Images: In medical imaging, healthcare professionals can use batch inference to look at a group (batch) of medical images, like X-rays or MRI scans, all at once. It helps spot problems or diseases quickly, making it easier to figure out the best treatment.

Autonomous Vehicles and Object Detection: Self-driving cars can use batch inferencing to look at many camera images at once. This helps the vehicle understand what’s around it, like other cars, people, or objects, so it can make smart, safe decisions.

Key Characteristics

Key characteristics of batch inferencing include:

  • Less Expensive: Predictions are made once per batch. The model makes predictions on a large number of inputs at once instead of one at a time.

  • Efficiency with Large Data Sets: Batch inferencing is particularly efficient with large datasets.

  • Scheduled Processing: Batch processing is often done on a schedule, like a nightly analysis of data collected throughout the day, rather than in real-time.

  • Resource Optimization: Since data is processed in large batches, computational resource efficiency is achieved when compared to real-time processing.