Page 10 - Ai Robogenius

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

Data
Deployment
Acquisition

Data
Evaluation
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.
By following the AI project cycle, you increase the chances of developing AI solutions that are not
only functional but also practical and impactful in real-world scenarios.

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