Inference

term_id: inference

Category: engineering_practice

Definition

Inference refers to the deployment stage where a finalized model is used to make decisions or predictions on unseen data. Unlike training, which updates weights, inference consumes computational resources to execute forward passes through the network. Optimizing inference is crucial for latency, cost, and scalability in production environments, often involving techniques like quantization, pruning, or batching to ensure efficient real-time performance.

Summary

The phase where a trained model processes new data to generate predictions or outputs.

Key Concepts

  • Prediction
  • Latency
  • Throughput
  • Deployment

Use Cases

  • Real-time fraud detection in banking transactions
  • Generating responses in live chatbot interactions
  • Classifying images in autonomous vehicle systems

Code Example

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import torch
model.eval()
with torch.no_grad():
    output = model(input_tensor)
    prediction = torch.argmax(output, dim=1)

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