In the field of artificial intelligence (AI), inference is the process where a trained machine learning model uses new data to draw conclusions.
Understanding AI Inference
AI inference is a fundamental step in deploying AI models in real-world applications. Based on the provided definition:
- It's a Process: Inference is an active operation performed by an AI model.
- Uses a Trained Model: This process happens after the AI model has been built and trained on historical data. Training teaches the model patterns and relationships; inference applies this learned knowledge.
- Input is Brand-New Data: The model takes data it has not seen before as input during inference.
- Output is Conclusions: The goal is for the model to make predictions, classifications, or generate results based on the input data.
- Operates Without Examples of Desired Result: A key characteristic is that the model can make these conclusions on new data without needing to be shown what the 'correct' answer for that specific piece of new data should be.
Essentially, AI inference is when an AI model puts its training to work, applying what it learned to make sense of or react to incoming information.
Key Aspects of Inference
- Application Phase: Inference is the "runtime" phase where the AI model is used to solve a problem or provide insights.
- Prediction & Decision Making: It's where models perform tasks like identifying objects in images, translating languages, predicting stock prices, or recommending products.
- Speed and Efficiency: Often, inference needs to be performed quickly, especially in real-time applications like autonomous driving or fraud detection.
To learn more about how inference compares to the training phase of AI development, you can refer to resources like the Cloudflare article on AI inference vs. training.
