Defining the problem: The first step in building an AI system is to define the problem that the system will be solving. This involves identifying the goals of the system and the specific tasks that it will be expected to perform.

Gathering and preparing data: In order to train an AI system, it is necessary to have a large dataset that the system can use to learn from. This dataset is typically collected and prepared by the development team, and it may include structured data (such as tables of information) or unstructured data (such as text or images).

Designing the model: Once the data has been collected and prepared, the development team must design the AI model that will be used to learn from the data. This typically involves selecting an appropriate algorithm or set of algorithms, as well as deciding on the architecture of the model (such as the number of layers and the number of units in each layer).

Training the model: Once the model has been designed, it must be trained using the collected data. This typically involves feeding the data into the model and adjusting the model's parameters based on the results. This process is usually done using specialized software and hardware that are optimized for training AI models.

Evaluating the model: Once the model has been trained, it must be evaluated to determine how well it is able to perform the tasks it was designed for. This typically involves testing the model on a separate dataset and comparing the results to the desired outcomes.

Fine-tuning and deployment: If the model performs well during evaluation, it can be fine-tuned and prepared for deployment. This may involve further training or adjustment of the model, as well as integrating the model into a larger system or application.