Reference 4 stops to get here

Request Batching

Combining multiple inference requests into batches to improve throughput.

Your route here

4 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Inference ✓ understood

    Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

  4. Batch Processing ✓ understood

    Processing multiple predictions together in batches rather than one at a time, improving throughput efficiency.

  5. Request Batching · you are here ✓ understood

Combining multiple inference requests into batches to improve throughput.

This concept is essential for understanding practical deployment and forms a key part of modern AI systems.

  • Inference
  • Batch Processing
  • Throughput

Where it sits

Before this

Inference Batch Processing
Request Batching

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Throughput The number of predictions or tokens a model can process per unit of time, a key deployment performance metric. Shipping AI Inference Latency The time delay between submitting input and receiving output from a deployed model, critical for real-time applications. Shipping AI Model Caching Storing frequently requested predictions to reduce latency and computation.