AI PROJECT CYCLE
The AI Project Cycle is a step-by-step process used to develop Artificial Intelligence projects. It
helps us to systematically build AI solutions to real-world problems.
5 main stages of the AI Project Cycle are:
1. Problem Scoping
Problem Scoping refers to understanding a problem finding out various factors which affect the
problem, define the goal or aim of the project.
Activities:
• Identify the problem statement.
• Define the goals of the AI solution.
• Understand who is affected by the problem
(stakeholders).
• Determine what success will look like
(evaluation criteria).
Example: You want to create an AI system to detect if a person is wearing a mask. Problem
scoping involves understanding why this is needed and what the system should achieve.
2. Data Acquisition
Goal: Data Acquisition is the process of collecting accurate and reliable data to work
with. Data Can be in the format of the text, video, images, audio, and so on and it can be
collected from various sources like interest, journals, newspapers, and so on.
Activities:
• Find or create datasets (images, text, numbers, etc.).
• Ensure data is relevant, clean, and reliable.
• Store data in a suitable format (CSV, Excel, etc.).
Example: Collect thousands of images of people with and without masks.
3. Data Exploration
Data Exploration is the process of arranging the gathered data uniformly for a better
understanding. Data can be arranged in the form of a table, plotting a chart, or making a
database.
Activities:
• Identify patterns, outliers, and errors in the data.
• Use graphs and charts (visualizations) to summarize key features.
The AI Project Cycle is a step-by-step process used to develop Artificial Intelligence projects. It
helps us to systematically build AI solutions to real-world problems.
5 main stages of the AI Project Cycle are:
1. Problem Scoping
Problem Scoping refers to understanding a problem finding out various factors which affect the
problem, define the goal or aim of the project.
Activities:
• Identify the problem statement.
• Define the goals of the AI solution.
• Understand who is affected by the problem
(stakeholders).
• Determine what success will look like
(evaluation criteria).
Example: You want to create an AI system to detect if a person is wearing a mask. Problem
scoping involves understanding why this is needed and what the system should achieve.
2. Data Acquisition
Goal: Data Acquisition is the process of collecting accurate and reliable data to work
with. Data Can be in the format of the text, video, images, audio, and so on and it can be
collected from various sources like interest, journals, newspapers, and so on.
Activities:
• Find or create datasets (images, text, numbers, etc.).
• Ensure data is relevant, clean, and reliable.
• Store data in a suitable format (CSV, Excel, etc.).
Example: Collect thousands of images of people with and without masks.
3. Data Exploration
Data Exploration is the process of arranging the gathered data uniformly for a better
understanding. Data can be arranged in the form of a table, plotting a chart, or making a
database.
Activities:
• Identify patterns, outliers, and errors in the data.
• Use graphs and charts (visualizations) to summarize key features.
