AI Project Cycle
AI Project Cycle
1) Problem Scoping: iden�fy the problem and have a vision to solve it. Problem scoping means selec�ng a
problem and finding a solu�on for it using AI technology.
2) 4Ws Problem canvas
The 4Ws Problem canvas helps you in iden�fying the key elements related to the problem.
Who?
Helps you in analysing the people ge�ng affected directly or indirectly due to it. You need to find out who the
‘Stakeholders’ to this problem are and what you know about them. Stakeholders are the people who face this
problem and would be benefited with the solu�on.
What?
You need to determine the nature of the problem. What is the problem and how do you know that it is a problem?
You also gather evidence to prove that the problem you have selected actually exists using Newspaper ar�cles,
Media, announcements, etc
Where?
Now that you know who is associated with the problem and what the problem actually is; you need to focus on the
context/situa�on/loca�on of the problem. This will help you look into the situa�on in which the problem arises, the
context of it, and the loca�ons where it is prominent.
Why?
Understand who the people that would be benefited by the solu�on are; what is to be solved; and where will the
solu�on be deployed. Think about the benefits which the stakeholders would get from the solu�on and how would
it benefit them as well as the society.
3) Problem Statement Template
The Problem Statement Template helps us to summarise all the key points into one single Template so that in
future, whenever there is need to look back at the basis of the problem, we can take a look at the Problem
Statement Template and understand the key elements of it.
4) Data Acquisi�on
Data can be a piece of informa�on or facts and sta�s�cs collected together for reference or analysis. Whenever we
want an AI project to be able to predict an output, we need to train it first using data.
You need to collect data from various reliable and authen�c sources. Since the data you collect would be in large
quan��es, you can use graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the
paterns in which your acquired data follows.
5) Types of Data:
a) Training data: It is the data on which we train our AI project model. It is basically to fit the parameters of
the project for the model.
AI Project Cycle
1) Problem Scoping: iden�fy the problem and have a vision to solve it. Problem scoping means selec�ng a
problem and finding a solu�on for it using AI technology.
2) 4Ws Problem canvas
The 4Ws Problem canvas helps you in iden�fying the key elements related to the problem.
Who?
Helps you in analysing the people ge�ng affected directly or indirectly due to it. You need to find out who the
‘Stakeholders’ to this problem are and what you know about them. Stakeholders are the people who face this
problem and would be benefited with the solu�on.
What?
You need to determine the nature of the problem. What is the problem and how do you know that it is a problem?
You also gather evidence to prove that the problem you have selected actually exists using Newspaper ar�cles,
Media, announcements, etc
Where?
Now that you know who is associated with the problem and what the problem actually is; you need to focus on the
context/situa�on/loca�on of the problem. This will help you look into the situa�on in which the problem arises, the
context of it, and the loca�ons where it is prominent.
Why?
Understand who the people that would be benefited by the solu�on are; what is to be solved; and where will the
solu�on be deployed. Think about the benefits which the stakeholders would get from the solu�on and how would
it benefit them as well as the society.
3) Problem Statement Template
The Problem Statement Template helps us to summarise all the key points into one single Template so that in
future, whenever there is need to look back at the basis of the problem, we can take a look at the Problem
Statement Template and understand the key elements of it.
4) Data Acquisi�on
Data can be a piece of informa�on or facts and sta�s�cs collected together for reference or analysis. Whenever we
want an AI project to be able to predict an output, we need to train it first using data.
You need to collect data from various reliable and authen�c sources. Since the data you collect would be in large
quan��es, you can use graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the
paterns in which your acquired data follows.
5) Types of Data:
a) Training data: It is the data on which we train our AI project model. It is basically to fit the parameters of
the project for the model.
