4. Modelling

After exploration of data, we need to look at different AI-enabled algorithms which can suitable for the project. We go through several models and select the ones which match the requirements. After choosing the model, we implement it. This is known as the Modelling stage of AI Project cycle.

The process of selecting and implementing the model which match the requirements of a project is known as the "Modelling".

TYPES OF AI Model

AI Model can be categorised as Rule Based AI Model (Model-driven AI Model) and Learning Based AI Model (Data-driven AI Model).

Rule-based AI Model: In Rule-based AI approach, the developer feeds the data along with the some ground rules as input to the AI model. This model then gets trained with these inputs and gives answers in the form of predictions. It is also known as Model-driven AI Model.

Learning-based AI Model: In Learning-based AI Approach, the developer feeds the data along with the answers to the AI Model. This model then designs its own algorithms and methodologies to match the data with answers and gives out the rules. It is also known as Data-driven AI Model.