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UNIT-2
AI PROJECT CYCLE
Learning Outcomes
• AI Project Cycle Framework • Iterative Nature of Problem Scoping
• AI Ethics Practiced while Designing AI Projects • Summary—AI Project Cycle
• Setting Goals for an AI Project • Identifying the Stakeholders
• 4Ws Problem Canvas • Problem Statement Template
• Data Acquisition • What is Data?
• System Maps • Data Visualisation
• Data Visualisation Tools • Different Ways to Visualise Data
• Visualise Data using Visualisation Tools • What is Modelling?
• Difference between AI, Machine Learning and Deep Learning • Data Modelling Techniques
• Decision Tree—Rule-Based Approach • Pixel It—Learning-Based Approach
• AI Project Evaluation • AI Project Deployment
AI is the top trending technology of this digital era. Most of the companies use AI to accomplish their mundane
tasks and achieve their company’s long-term goals. As we know that AI enables us to make smart machines. It
allows machines to perform tasks that are difficult to perform for human beings. The process of developing AI
machines has different stages that are collectively known as AI project cycle.
In the previous unit, you have learnt about artificial intelligence and its applications. You have also learnt about
domains of AI and Sustainable Development Goals. In this chapter, you will learn about AI project cycle and its
different stages.
AI Project Cycle Framework
Before learning about AI project cycle framework, let us first learn about traditional software development cycle.
Traditional software development follows the Software Development Life Cycle (SDLC).
AI Project Cycle 209

