Model training

AI model training starts with data. While the actual size of the dataset depends on the project, all machine learning projects require high-quality, well-annotated data to succeed. One of the computer science rules is garbage in, garbage out. To start model training AI is given a set of training data and asked to make decisions based on that information. As mistakes are made, adjustments can be made to the model to help the AI become more accurate.

Once the AI has completed basic training, it can begin the validation phase. In this phase, data is validated using a new data set, and any adjustments are made depending on the results. Then the testing phase is reached by conducting a real-world test. Giving the AI a dataset that does not include any tags or targets (training data that help the AI interpret the data). If the AI model makes accurate decisions based on this unstructured information, it has passed its training phase. If not, the model training process is repeated until the AI performs as expected.