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Class 9 AI Project Cycle: cheat sheet
CBSE Computational Thinking and AI. The AI project cycle has six stages: problem scoping, data acquisition, data exploration, modelling, evaluation and deployment.
Key ideas
- Problem scoping uses the 4Ws canvas to say clearly who has the problem, what it is, where it happens and why solving it helps.
- Data acquisition collects reliable data on the features that affect the problem, such as rainfall for crop yield.
- Data exploration uses graphs and summaries to find patterns, gaps and odd values before modelling.
- A rule-based model follows rules written by people; a learning-based model finds its own rules from data.
- Labelled data is split into a training set for learning and a test set kept back for evaluation.
- Deployment puts the model into real use, in an app or a system, where it must keep being checked.
Remember
| The six stages | problem scoping → data acquisition → data exploration → modelling → evaluation → deployment |
| 4Ws | Who is affected? What is the problem? Where does it arise? Why will solving it help? |
| Train : test split 80 : 20 of 500 | 400 for training, 100 for testing |
| Readings in a day | 1,440 minutes ÷ minutes between readings |
| Accuracy on the test set | correct ÷ test examples × 100% |
Watch out
- Jumping to modelling before the problem is scoped and the data explored.
- Testing on the training data, which makes a model look better than it is.
- Calling a fixed-rule program learning-based.
Worked example
Put the stages of the AI project cycle in order. Write the letters in order. (a) Problem scoping (b) Deployment (c) Data acquisition (d) Data exploration (e) Evaluation (f) Modelling
Working: Problem scoping → Data acquisition → Data exploration → Modelling → Evaluation → Deployment
Answer: a, c, d, f, e, b
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