The AI project cycle is a step-by-step process that a person should follow to develop an AI project to solve a problem. It provides an appropriate framework that can lead to achieving the goal of the project. The AI project cycle mainly has five stages: problem scoping, data acquisition, data exploration, modeling, and evaluation.
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Problem Scoping: This stage involves understanding the problem and defining the scope of the project. It includes identifying the problem, defining the objectives, and determining the requirements for the project.
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Data Acquisition: This stage involves collecting accurate and reliable data that will be used to train the AI model. The data can be obtained from various sources, such as databases, sensors, or the internet.
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Data Exploration: This stage involves arranging the data uniformly and exploring it to identify patterns and relationships. It includes data cleaning, data transformation, and data visualization.
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Modeling: This stage involves creating models based on the visualized data. It includes selecting the appropriate algorithm, training the model, and testing it.
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Evaluation: This stage involves evaluating the performance of the model and making improvements if necessary. It includes measuring the accuracy, precision, and recall of the model.
The AI project cycle is a process of steps involved in preparing an AI model or AI project. It is somewhat related to IT projects but not IT projects. The IT project has following steps: design, develop, test, and deploy. The AI project cycle is more complex and includes additional stages such as problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment.
