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• Problem Scoping: The first stage of an AI project cycle is problem scoping to identify the problem and have
a vision to solve it. Problem scoping refers to understanding a problem and various factors which affect the
problem, and finding a solution for it using AI technology. The 4W’s of problem scoping are Who, What, Where,
and Why. These Ws help in identifying and understanding the problem in a better and efficient manner.
• Data Acquisition: The next stage of the AI project cycle is data acquisition. The term data acquisition means
collecting raw data for the purpose of reference or analysis for the project. The data can be in the form of
text, numbers, images, videos, or audio and it can be collected from various sources like Internet, journals,
newspapers, and so on. The data acquisition system allows us to obtain valuable information about reality to
improve the performance of the project.
• Data Exploration: Data exploration refers to the techniques and tools used to visualise data collected in data
acquisition through complex statistical methods. It is the process of analysing a large dataset.
• Modelling: It is the design phase of the AI project cycle. In this, we select the best way to reach the solution.
It requires the process of selecting the right algorithm to develop a working model for the project. In this
step, different models based on the visualised data can be created and even checked for the advantages and
disadvantages of the model.
• Evaluation: It is the testing phase of the AI project cycle, where we check if the model can achieve required
goals or not. If the model is not fulfilling the requirements, the model or even the data can be changed. Once
the developer feels the project is ready, the project will be put into working conditions and then deployed and
handed over to the user. If the deployment stage is not reached, the project is of no use.
• Deployment: In this stage, we integrate the best-performing model into the production environment, setting
up continuous monitoring, and maintenance to sustain performance over time.
Reboot
1. List down the steps of AI project cycle.
2. Differentiate between evaluation and deployment in an AI project cycle.
Why We Need an AI Project Cycle?
The AI project cycle is a structured framework comprising stages from problem definition and data acquisition to
model development and deployment. It involves identifying objectives, gathering data, exploring and modelling
data, evaluating outcomes, and deploying AI solutions. This iterative process ensures systematic development,
validation, and improvement of AI applications aligned with business goals and user needs. We use an AI project
cycle for these important reasons:
• Structure and Organisation: Provides a clear roadmap and systematic approach for planning,
executing, and managing AI projects, ensuring all steps are followed in a logical sequence.
• Efficiency: Optimises resource allocation, time management, and task prioritisation throughout
the project lifecycle, leading to more effective and timely project outcomes.
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