Batch Processing
Method of processing multiple data samples simultaneously to improve computational efficiency during AI model training and inference.
Definition
Batch processing groups multiple data samples into batches for parallel processing, leveraging GPU and CPU architectures more efficiently than processing individual samples sequentially.
This approach reduces training time, enables better gradient estimates, and improves hardware utilization, though batch size selection affects memory usage and model convergence characteristics.
Why It Matters
Efficient batch processing reduces AI model training and inference costs by maximizing hardware utilization, enabling businesses to deploy AI solutions faster and more economically.
Optimized batch processing strategies allow businesses to handle larger datasets and more complex models within existing computational budgets, expanding AI capabilities without proportional cost increases.
Examples in Practice
Cloud service providers optimize batch processing for customer AI workloads, offering cost-effective training solutions that can handle massive datasets for image recognition and natural language processing tasks.
Manufacturing companies use batch processing in quality control AI systems, analyzing hundreds of product images simultaneously to detect defects efficiently during production runs.
Financial institutions implement batch processing for risk assessment models, evaluating thousands of loan applications overnight using parallel processing to reduce decision turnaround times.