The steps involved in AI project cycle are as given:
The first step is Scope the Problem by which, you set the goal for your AI project by stating the problem which you wish to solve with it. Under problem scoping, we look at various parameters which affect the problem we wish to solve so that the picture becomes clearer
Next step is to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping.
Next, you go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns in which your acquired data follows.
After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output.
You can test the selected models and figure out which is the most efficient one.
The most efficient model is now the base of your AI project and you can develop your algorithm around it.
Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and hence improving it.
Finally, after evaluation, the project cycle is now complete and what you get is your AI project.
