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Module 04 · Building and Iterating AI Products
Defining the AI MVP for 'RetailFlow' Recommendation Engine
You are the AI Product Manager at 'RetailFlow'. Leadership wants a 'smart' recommendation engine to boost sales. You must move away from 'Data Perfectionism' and define a fast, experimental MVP that uses existing data and addresses the 'Cold Start' problem without waiting months for a perfect model.
45 minBeginner 4 outcomes 6 steps · 3 checkpoints
lab progress0/9 · 0%
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Knowledge check
5 questions from this module's lessons.
0/5 correct
- 01
According to the context, what is the primary reason many AI projects fail when treated like traditional software?
- 02
What is the key transition required when moving from feature-based planning to data-centric roadmapping?
- 03
Why does traditional Agile methodology need adaptation for AI development teams?
- 04
The 'Rosetta Stone' for PM-to-Data Science collaboration is intended to solve which specific problem?
- 05
According to Gartner, what is the root cause of over 80% of AI project failures?
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Dataset
retail_traffic_silver.csv
'Silver Data' (raw, messy logs) from recent customer purchases. 10 rows with some missing values and inconsistent categories to simulate real-world 'messy' conditions.
Contains one duplicate (User 101) and one null value (User 104) to represent 'Silver Data'.
| user_id(integer) | item_purchased(string) | category(string) | timestamp(string) |
|---|---|---|---|
| 101 | Wireless Mouse | Electronics | 2023-10-01 10:00 |
| 102 | Yoga Mat | Fitness | 2023-10-01 10:05 |
| 103 | Laptop Stand | electronics | 2023-10-01 11:20 |
| 104 | Coffee Beans | 2023-10-01 12:15 | |
| 105 | Desk Lamp | Home | 2023-10-01 13:00 |
| 101 | Wireless Mouse | Electronics | 2023-10-01 10:00 |
| 106 | Running Shoes | Fitness | 2023-10-01 14:10 |
| 107 | Notebook | Office | 2023-10-01 15:30 |
| 108 | Headphones | Electronics | 2023-10-01 16:45 |
| 109 | Water Bottle | Fitness | 2023-10-01 17:20 |