In-depth Explanation of AI Model Training

AI model training is an iterative process whose success depends on the quality and depth of the input data, as well as the ability of the trainer to identify and address deficiencies. Data scientists typically handle the training process, although business users may also be involved in some low-code/no-code environments. In fact, the cycle of processing, observing, providing feedback, and improving is similar to teaching a child a new skill. The goal of AI model training is to create a mathematical model that can accurately generate outputs while balancing the many potential variables, outliers, and complex factors within the data. Think of it like raising a child, only much more complex.

Consider how children learn a skill. For example, suppose you want to teach a toddler to distinguish between cats and dogs. First, you provide some basic pictures and encouragement. Then, you introduce more variables, such as average size, barking versus meowing, and behavioral patterns. Based on the challenges the child may encounter, you may focus on specific aspects to promote learning. By the end of the process, the toddler should be able to recognize a variety of cats and dogs, from common household pets to wild animals.

Training an AI model is similar.

AI: Choose an algorithm and an initial training dataset for the model.
Child: Use basic pictures to determine the general difference between dogs and cats.
AI: Evaluate the output accuracy and adjust the model to reduce or eliminate certain inaccuracies.
Child: Offer praise or correction based on the answer.
AI: Provide an additional dataset with specific, diverse inputs to fine-tune the model.
Child: Highlight different features, shapes, and sizes as part of the learning process.

Just like with children, the initial AI model training has a significant impact on future development — and whether further learning is needed to eliminate undesirable effects. This highlights the importance of high-quality data sources, whether for initial training or continuous iterative learning after the model has been deployed.