Reference 3 stops to get here

Model Caching

Storing frequently requested predictions to reduce latency and computation.

Your route here

3 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. Model Caching · you are here ✓ understood

Storing frequently requested predictions to reduce latency and computation.

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

  • Inference
  • Performance
  • Optimization

Where it sits

Before this

Inference
Model Caching

Leads to

Nothing yet: a destination in its own right.

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Shipping AI Request Batching Combining multiple inference requests into batches to improve throughput. 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 Serving Deploying trained models as services that can handle prediction requests in production environments. Shipping AI Throughput The number of predictions or tokens a model can process per unit of time, a key deployment performance metric.