These are very simple steps to understand the core of building any no code AI model. Below I will show step by step guide choose which will help you find the tool you need for your business.
Step one: Write what is the business problem. Describe the pinpoint “why is it important to solve it”, “who are the people impacted” and if you have any thoughts on the solution, include what is the starting point to solve this problem. We don’t need the solution yet. Example of a business problem is we want to identify which set of customers to send the promotion coupon. This affects our customers who will be part of a new campaign and the marketing department will execute this campaign selecting the right side of people will impact revenues and conversion from the campaign. We can start solving this problem by looking at last year’s campaign list of customers to understand what factors contributed to a successful campaign.
Step two: Write down why the solution to this problem is predictive? If you can list the steps to solve the problem and it can be solved by a program that is a prescriptive problem. So think about whether this problem is predictive and requires an AI to look at lots and lots of historic data to understand which set of customers will have a higher propensity to click on a coupon from the campaign.
Step three: List all the types of data you have related to the problem that could possibly be used to train the AI. For example, you might want to list marketing forecast, past campaign records, anything that might be of use, we do not need the actual date of the stage we want to list all data that you have that could be used to solve the problem.
Step four: It is to collect the data required to train the model. All AI is trained with data, the same is true of No code AI. So the first step to solving a problem with AI is the collect the data required to train the AI. This is where you will collect actual data and collect a variety of data in huge volume from all possible sources.
Step five: It is to organize the data. This is a critical step before we build to model. The AI has to learn – What is good and what is bad? so we need to collect a large volume of clean data and organize it into groups or classes. A large volume of data will provide more accurate training of the AI model. This step to clean and organize data to train AI is called Data Engineering.
Step six: train the model using the appropriate no code AI tool. In this step we pick the tool which is available to work on the model based on above steps. For Example, if the requirement is to collect the images and build the model to segregate the images with the class name, we will use Computer vision.