What is AI Inference?

If **Training** is the long, expensive process of teaching an AI model, then **Inference** is the fast, efficient process of actually using that trained model to do its job.

Step 1: The Trained Model is Ready

The hard work is already done. A model has been trained on millions of data points and is now a ready-to-use tool. Think of it like a fully stocked vending machine, waiting for a customer.

Step 2: A New, Unseen Input Arrives

Now, you give the model a new piece of data it has never seen before—a photo to classify, a sentence to complete, a question to answer. This is the "coin" you put into the vending machine.

Step 3: The "Forward Pass"

The model takes the input and runs it through its network in a single, rapid calculation known as a "forward pass." It applies all the patterns it learned during training to the new data. This is the machine "processing" your request.

Step 4: The Prediction is Delivered

The model instantly produces an output: a prediction, a classification, or a generated sentence. The vending machine dispenses its product. This entire inference process is incredibly fast, often taking less than a second.

Training vs. Inference

This is the key distinction:

• Training is slow, expensive, and done once (the learning phase).
• Inference is fast, cheap, and done millions of times (the working phase).

Inference is what makes AI useful in real-time applications like search engines and voice assistants.