Reference 3 stops to get here · leads to 1
Batch Processing
Processing multiple predictions together in batches rather than one at a time, improving throughput efficiency.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- 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.
- 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.
- Batch Processing · you are here ✓ understood
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Shipping AI Request Batching Combining multiple inference requests into batches to improve throughput. Shipping AI Throughput The number of predictions or tokens a model can process per unit of time, a key deployment performance metric. Shipping AI Model Serving Deploying trained models as services that can handle prediction requests in production environments. Shipping AI Inference Latency The time delay between submitting input and receiving output from a deployed model, critical for real-time applications.