Definition: AI training vs. inference

The simplest definition is that training is about learning something, and inference is applying what has been learned to make predictions, generate answers and create original content. However, although a lot goes into both stages, inference is "where the magic is."

A machine learning (ML) model is the foundation of chatbots such as ChatGPT and Gemini. After its neural network architecture is designed and programmed, it is fed samples of data, which can include a highly selected amount of data or nearly all the information in the world.

The training and fine-tuning stages analyze the patterns in the data. This can take from days to months in datacenters with tens to hundreds of thousands of servers operating together. For example, GPT-4 took several months and was trained on trillions of words. See AI training and AI model.

Inference Processing
The AI application that people use (ChatGPT, Gemini, etc.), is called the "inference engine," a term that dates back to the first AI programs (see expert system). Once programmed, the inference engine may be able to use different models for various purposes.

To generate output from the questions people enter, the inference engine essentially "reverses" the model based on algorithms and a lot of adjustments made during its programming phase.

Running an inference engine to get a single answer takes a minuscule amount of time compared to the training phases of model development. However, consider that millions of people may be simultaneously using the same AI chatbot (same inference engine); thus, in the long run, inference processing may add up to much more computer time than the training phases of the models used. See neural network and AI datacenter.


 REGULAR DATA PROCESSING DEVELOPMENT:

 1. design the logic
 2. code the logic
 3. test application
 4. run application

 --------------------------------------------

 AI NEURAL NETWORK DEVELOPMENT:
 (see AI secret sauce).

 Model Development

 1.  INITIAL DESIGN
 1a.  select network type (CNN, RNN, GAN, etc.)
 1b.  code the model
 1c.  set layers, neurons, passes (hyperparameters)
 1d.  software sets weights and biases (parameters)

 2.  PRE-TRAIN (with example datasets)
 2a.  hyperparameters mostly adjusted by people
 2b.  parameters adjusted by software

 3.  FINE-TUNE (with example datasets)
 3a.  hyperparameters mostly adjusted by people
 3b.  parameters adjusted by software
      See AI hyperparameter.


 Inference Engine

 1.  design
 2.  code
 3.  optimize (see AI quantization)


 Execute AI Application

 1.  run inference engine with built-in model
      or
 2.  run inference engine and select model

A Clear-cut Comparison

This clever comparison of machine learning programming vs. traditional programming comes from Techopedia's "The Ultimate Guide to Applying AI in Business."

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Definition: AI training vs. inference