Page 30 - Ai V2.0 Flipbook C8
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Overview of Stages of AI Project Cycle
By defining the problem, gathering relevant data, analysing it, building a model, and evaluating
its performance, the AI project cycle helps ensure your project meets its objectives. This cycle
offers a structured roadmap for creating effective AI solutions.
The following figure shows the stages of AI Project Cycle:
Problem Scoping
Deployment Data Acquisition
Evaluation Data Exploration
Modelling
The description of the stages of AI project cycle is as follows:
• Problem Scoping: This is the first step where the problem is identified and clearly defined using
the 4Ws—Who, What, Where, and Why. It helps in understanding the issue and planning how AI
can solve it.
• Data Acquisition: In this stage, relevant raw data (text, images, videos, etc.) is collected from
sources like the Internet, books, or surveys to support the project.
• Data Exploration: The collected data is analysed and visualised using statistical tools to
understand patterns, trends, and insights.
• Modelling: Suitable algorithms are selected to build and test models that can solve the problem
effectively. Different models are compared to choose the best fit.
• Evaluation: The model is tested to check its performance. If it fails to meet the goals, data or the
model may be adjusted. Once ready, the model proceeds to deployment.
• Deployment: The final model is implemented in a real-world environment with ongoing
monitoring and maintenance to ensure continued success.
These stages help in systematically address challenges from the initial problem identification.
It ensures that data collected is relevant and complete, which contributes to building a reliable
model and gaining meaningful insights.
28 Touchpad Artificial Intelligence (Ver. 2.0)-VIII

