AI Inference
· 2 min read
AI Inference is the process through which an artificial intelligence model applies what it has learned during training to make predictions, classifications, or decisions based on new input data.
How AI Inference Works
- Trained Model: An AI model is trained on a dataset. During training, it learns patterns and relationships from the data.
- Inference: Once trained, the model is used to make predictions on previously unseen data.
Example:
- A computer vision model trained to recognize images of cats (training phase) receives a new image and determines whether it contains a cat or not (inference phase).
Key Features of AI Inference
- Efficiency: Fast and optimized for real-time or resource-constrained environments
- Deployment: Runs on edge devices (smartphones, IoT sensors) or cloud environments
- Optimization: Uses techniques like quantization to improve performance
AI Inference vs Training
| Aspect | Training | Inference |
|---|---|---|
| Objective | Learn from labeled data | Make predictions |
| Complexity | High (needs GPU/TPU) | Lower |
| Time | Hours/days | Milliseconds |
| Environment | Data centers | Cloud/edge devices |
Common Applications
- Speech Recognition: Virtual assistants like Alexa
- Computer Vision: Self-driving cars, surveillance
- Recommendations: Netflix, Amazon suggestions
- Translation: Google Translate