AI project lifecycle explained by an analogy- two examples
A. Here’s an analogy using a science fair project to explain the AI project cycle : 1. Problem Scoping → Choosing a Science Fair Project Topic Before starting a science project, you first decide what problem you want to solve. For example, "How can we make plants grow faster?" 2. Data Acquisition → Collecting Materials & Information You gather what you need—seeds, soil, water, and information on plant growth. In AI, this step is like collecting data from various sources. 3. Data Exploration → Observing and Preparing the Data Before planting, you check if your soil is good, your seeds are healthy, and if anything needs to be adjusted. In AI, this is where you clean and analyze data to make sure it’s useful. 4. Modelling → Conducting the Experiment You plant seeds in different conditions (e.g., more sunlight, less water) and observe the growth. In AI, this is like training different models to see which one works best. 5. Evaluation → Checking the Results After ...
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