AI Model

AI model training is the process of feeding an AI model curated data sets to evolve the accuracy of its output. The process may be lengthy, depending on the complexity of the AI model, the quality of the training data sets, and the volume of training data. Once the training process passes a benchmark for expected successes, data scientists continue to monitor results. If accuracy dips or the model has difficulty handling certain types of situations, the model may require further training.

Anyone with access to the proper tools can train an AI model using any PC, assuming they have access to the needed data. The steps include identifying the problem, selecting the training model, finding training data sets, and running the training processes. This can be on a small, local scale or a large enterprise scale depending on the scope of the project and resources available. New or independent developers can take advantage of cloud services that provide CPU resources across a variety of programming languages and remove geography from the equation.

The cost of training an AI model depends on the project’s scope. Across the industry, costs continue to trend downward as CPU/GPU power and cloud access provide more resources. In fact, the average training cost for a small project, such as image classification, was $1,000 in 2017 but only $5 in 2022, according to Stanford’s Institute for Human-Centered Artificial Intelligence AI Index. In comparison, the cost for large enterprise AI projects is actually increasing. For example, something like ChatGPT training can require an estimated budget of $3 million to $5 million. This disparity comes down to the complexity of projects and the fact that growing resources make increasingly complex and boundary-pushing projects available—if you can afford them.

In general, the four types of AI models are the following: - Supervised learning uses established training data sets and defined parameters to train the model. - Unsupervised learning involves an AI system ingesting an unlabeled data set without parameters or goals and determining patterns in the data. - Reinforcement learning models start with experimental decisions that lead to positive or negative reinforcement. After time, the AI learns the “best” course of action. - Generative learning is an unsupervised AI method that uses a large amount of example data to create a prompted output. Some data scientists also use transfer learning, where an existing AI model is a starting point for a new model, and semi-supervised learning, which melds supervised and unsupervised learning.