Model development process

The development of an artificial intelligence model involves analyzing the problem, finding suitable the principles of algorithms algorithmic principles, and ultimately training Machine learning model. Taking machine learning models as an example, the model development process can be divided into five steps: defining the model's purpose, data preprocessing, model training, model evaluation, and model deployment.

Defining the purpose of the model involves analyzing what problem the artificial intelligence intends to solve and finding the appropriate algorithms to address it. Since machine learning models require data for training, it is necessary to prepare and preprocess the training data beforehand.

For beginners, the most challenging aspects are model evaluation and deployment. Evaluating the performance of a model accurately, considering it is trained on historical data, can be difficult and requires a lot of techniques and tools. Finally, as the ultimate goal of an artificial intelligence model is to serve humans, it must be deployed as a product rather than remaining a mathematical model in a laboratory. Deploying the model efficiently, ensuring stability, and handling high traffic demands become the greatest challenges for every AI engineer.