Batch Processing

Batch processing means collecting multiple inputs and processing them together as a single group rather than handling each one individually. In AI, this applies to both training and inference.

Batch processing during training

When training a model, you rarely feed one example at a time. Instead, you group examples into batches — say, thirty-two or sixty-four at once. The model processes the entire batch, calculates the average error across all examples, and updates its weights once. This is far more efficient than updating after every single example.

Batch size trade-offs

• Larger batches train faster because GPUs can process many examples in parallel, but they use more memory and can sometimes lead to less effective learning
• Smaller batches introduce more noise into the training process, which can actually help the model generalise better, but training takes longer
• Mini-batch is the common middle ground — not the full dataset, not a single example, but a manageable chunk

Batch processing during inference

When deploying AI in production, batch processing means collecting multiple requests and running them through the model together. An email classification system might batch-process all emails received in the last five minutes rather than classifying each one individually. This reduces cost because you make fewer API calls and use GPU resources more efficiently.

Batch vs. real-time processing

• Batch processing is ideal for tasks where a slight delay is acceptable: nightly report generation, bulk document classification, periodic data analysis
• Real-time (streaming) processing is necessary when immediate responses matter: chatbots, live fraud detection, voice assistants

Many AI systems use both: real-time for user-facing interactions and batch for background tasks like model retraining or bulk analysis.