Class 8
Chapter 1: AI Project Lifecycle
Every AI project follows a similar journey: define the problem, collect data, prepare the data, train a model, evaluate it, and deploy it. After deployment, you keep monitoring and improving it.
Learning outcomes
- List the stages of an AI project (problem → data → model → deploy)
- Apply the lifecycle to a small classroom problem
Activities
- Plan an AI project canvas in groups
Worked examples
Read through these first, then try the practice below.
Example 1 — Stage spotter
You photograph 200 leaves and label each one as healthy or diseased so the model can learn. Which stage of the lifecycle are you in?
Solution: Data collection and preparation — you are gathering the labelled examples the model will learn from.
Example 2 — Why we evaluate
Your trained model says it is 99% accurate on the training set but only 60% accurate on new pictures. What is happening?
Solution: The model has memorised the training set rather than learning the pattern. You need more diverse data and to evaluate on a separate test set.
Self-do practice
Question 1 of 3 · Score 0/0Which is the FIRST stage of an AI project lifecycle?
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