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.

Modelling is the process of selecting and implementing the suitable AI-enabled algorithms for the success of project.

Explanation

After exploration of data, we need an AI-enabled algorithms or AI models that helps to feed or store that data into the project/system. This models help to store the data and train the machine to work.
This could be done either by designing your own AI model or by using the pre-existing AI models.

Let us learn about the difference between AI (Artificial Intelligence), ML (Machine Learning) and DL (Deep Learning):-

Artificial Intelligence (AI)

AI refers to any technique that enables computer to mimic human intelligence. AI machine works on algorithms and data feed into it.

Machine Learning (ML)

ML enables machines to improve upon tasks with experience. The machines learns itself.

Deep Learning (DL)

DL enables the software to train itself to perform tasks with a vast amount of data. It is able to train itself with the help of multiple machine learning algorithms.