oc-p12-fashion-insta/README.md

# AI Project Scoping - Fashion-Insta

📘 Ce projet est également disponible en [français 🇫🇷](./README.fr.md)

**Source of truth** : https://git.gregoiremureau.com/openclassroom-ai/oc-p12-fashion-insta

```bash
git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p12-fashion-insta.git
```

Dataset: see `fetch-data.sh` (outside git if large).

## 📋 Project Context

This project was carried out as part of an **AI Product Manager** assignment at **Fashion-Insta**, a fashion company that sells clothing items through a network of physical stores and an e-commerce website.

The objective was to carry out the complete scoping of a strategic AI project: the development of a mobile application for recommending clothing items based on photos, in order to present the project to the executive committee (COMEX) for approval.

## 🎯 Objectives

The project aims to develop a **mobile recommendation application** allowing users to:
1. Take a photo of themselves wearing their favorite clothes
2. Get recommendations for items in the same clothing style
3. Boost online and in-store sales

**Mission assigned**: Carry out the entire scoping of the project following an agile SCRUM methodology and convince the COMEX to launch the project.

## 📊 Scoping Approach

The project scoping took place over several phases across 3 weeks:

### Phase 1: Requirements gathering and backlog
- Formalization of user stories based on business needs
- Prioritization using the MoSCoW method (Must have, Should have, Could have, Won't have)
- Identification of the MVP (Minimum Viable Product)
- Review with the Product Owner

### Phase 2: Estimation and profitability
- Estimation of effort per user story (in person/days)
- Breakdown by profile (Data Scientist, Developer, DevOps, etc.)
- Calculation of development costs
- Estimation of Azure costs (initial infrastructure and production)
- Profitability analysis (ROI, break-even point)
- Sprint planning

### Phase 3: GDPR compliance and risks
- Analysis of personal data (CNIL data processing register)
- Identification of legal and ethical issues
- Analysis of potential model biases
- Complete project risk analysis
- Mitigation action plan

### Phase 4: COMEX presentation
- Summary of objectives and expected gains
- Presentation of the agile SCRUM methodology
- Sprint planning and organization
- GDPR and ethical issues
- Risk mitigation

## 📁 Project Structure

```
├── P12_01_backlog.xlsx
├── P12_02_tableur.xlsx
├── P12_03_présentation.pdf (ou .pptx)
├── P12_04_ExportedEstimate_initial.xlsx
└── P12_05_ExportedEstimate_production.xlsx
```

### 1. `P12_01_backlog.xlsx`

**Project backlog with prioritized user stories**

This file contains:
- **User stories**: Formalization of functional and technical needs from the user's point of view
- **MoSCoW prioritization**:
  - **Must have**: Features essential to the MVP
  - **Should have**: Important but non-blocking features
  - **Could have**: Desirable features
  - **Won't have**: Features excluded from the current scope
- **Effort estimation**: Development time per user story
- **Acceptance criteria**: Validation conditions for each story
- **Dependencies**: Relationships between user stories

**Focus**: AI user stories (recommendation, image processing, ML models)

### 2. `P12_02_tableur.xlsx`

**Spreadsheet for effort and cost estimation**

This file includes several tabs:

**"Effort by profile" tab**:
- Breakdown of effort by user story
- Profiles: Data Scientist, ML Engineer, Full-Stack Developer, DevOps, Product Owner, Scrum Master
- Calculation of person/days per profile
- Application of daily rates (TJM)

**"Development costs" tab**:
- Total initial development cost
- Breakdown by profile and by sprint
- Fixed vs variable costs

**"Recurring costs" tab**:
- Annual maintenance: 15% of development cost
- Production Azure infrastructure costs (annual)
- Cost trends over 3-5 years

**"Profitability analysis" tab**:
- Estimated annual gains (increase in sales)
- Cumulative costs year after year
- ROI and break-even point calculation
- Profitability chart

**"Sprint planning" tab**:
- Breakdown into sprints (duration, content)
- Assignment of user stories per sprint
- Milestones and deliverables

### 3. `P12_03_présentation.pdf` (or .pptx)

**Presentation for the COMEX**

This presentation includes:

**Slide 1: Executive summary**
- Summary of key points (not presented orally)
- Project vision in one slide

**Slides 2-3: Objectives and expected gains**
- Increase in sales (quantified)
- Improved customer experience
- Success KPIs (adoption rate, conversion rate, satisfaction)

**Slides 4-5: Required resources**
- Human: Team and profiles needed
- Technical: Azure infrastructure
- Financial: Initial investment and recurring costs

**Slides 6-8: Agile SCRUM methodology**
- Principles of agility
- Roles (Product Owner, Scrum Master, Development Team)
- Benefits for the project

**Slides 9-11: Organization and tracking**
- Daily Scrum (daily standup)
- Sprint Review (end-of-sprint demo)
- Retrospective (continuous improvement)
- Burndown chart (progress tracking)

**Slides 12-13: Sprint planning**
- Time breakdown
- Content of each sprint
- Key milestones and deliverables

**Slides 14-16: Legal and ethical issues**
- GDPR principles
- Personal data management (photos, preferences)
- Potential model biases (diversity, representativeness)
- Transparency and consent

**Slides 17-19: Risks and mitigation**
- Technical risks (model performance, scalability)
- Organizational risks (team availability)
- Financial risks (budget overrun)
- Legal risks (GDPR non-compliance)
- Action plan for each critical risk

**Appendix: Full backlog**
- List of user stories with prioritization
- MVP user stories clearly identified

### 4. `P12_04_ExportedEstimate_initial.xlsx`

**Azure cost estimation for the initial phase**

This file contains the estimation of Azure cloud infrastructure costs for:
- **Design phase**: Development environment
- **Model training phase**:
  - Compute instances (GPU for deep learning)
  - Training data storage
  - ML services (Azure Machine Learning)
  - Computer vision services (Computer Vision API)

**Export**: Azure Pricing Calculator

### 5. `P12_05_ExportedEstimate_production.xlsx`

**Azure cost estimation for production**

This file contains the estimation of Azure cloud infrastructure costs for:
- **Application production**:
  - Compute instances for the recommendation API
  - Database (product catalog, user history)
  - Blob storage (user images)
  - CDN (Content Delivery Network)
  - ML inference services
  - Monitoring and logging services
  - Bandwidth

**Calculation**: Monthly and annual costs based on a traffic estimate

## 🛠️ Methodology

### Agile SCRUM Method

**Roles**:
- **Product Owner**: Alicia (VP Product)
- **Scrum Master**: To be defined
- **Development Team**: Data Scientists, ML Engineers, Developers, DevOps

**Ceremonies**:
- **Sprint Planning**: Sprint planning (1-2 weeks)
- **Daily Scrum**: 15-minute daily standup
- **Sprint Review**: End-of-sprint demonstration
- **Sprint Retrospective**: Continuous improvement

**Artifacts**:
- **Product Backlog**: Prioritized list of user stories
- **Sprint Backlog**: User stories selected for the sprint
- **Increment**: Potentially shippable product at the end of the sprint

### MoSCoW Prioritization

- **Must have (M)**: Essential to the MVP, non-negotiable
- **Should have (S)**: Important but can be postponed
- **Could have (C)**: Desirable if budget/time available
- **Won't have (W)**: Excluded from the current scope

### Effort Estimation

**Techniques used**:
- Planning Poker for collaborative estimation
- Story Points or Person/Days depending on the user stories
- Team velocity to adjust forecasts

## 📈 Profitability Analysis

### Costs

**Initial investment**:
- Development: Total cost of sprints
- Azure infrastructure (initial phase)
- Team training

**Recurring costs (annual)**:
- Maintenance: 15% of development cost
- Azure infrastructure (production)
- Support and enhancements

### Gains

**Estimated gains**:
- Increase in online conversion rate
- Increase in average basket size
- Customer retention (churn reduction)
- Cross-selling and up-selling

**ROI calculation**:

```
ROI = (Gains cumulés - Coûts cumulés) / Coûts cumulés × 100
```

**Break-even point**: Date at which cumulative gains equal cumulative costs

### Visualization

Chart showing:
- Cumulative costs curve
- Cumulative gains curve
- Break-even point (intersection of the curves)

## 🔒 GDPR Compliance

### Personal data processed

- **User photos**: Sensitive data (images of people)
- **Clothing preferences**: Behavioral profiling
- **Browsing history**: Tracking of interactions
- **Account data**: Email, name, contact details

### Data processing register (CNIL)

For each AI processing activity:
- **Purpose**: Personalized item recommendation
- **Data categories**: Images, preferences, behavior
- **Retention period**: Defined and justified
- **Security measures**: Encryption, pseudonymization
- **Recipients**: Data team, partners (if applicable)
- **Transfers outside the EU**: Azure Europe region (compliance)

### GDPR Principles Applied

- **Explicit consent**: Opt-in for the use of photos
- **Right of access**: Users can view their data
- **Right to erasure**: Deletion of data upon request
- **Portability**: Export of personal data
- **Minimization**: Collection of only necessary data
- **Transparency**: Clear information about data use

## ⚖️ Ethical Issues

### Model bias

**Identified risks**:
- **Representation bias**: Under-representation of certain body shapes, skin colors, clothing styles
- **Cultural bias**: Recommendations skewed toward Western standards
- **Gender bias**: Gendered clothing stereotypes

**Action plan**:
- Diversification of the training dataset
- Regular audit of recommendations
- User testing on varied populations
- User feedback capability to improve the model

### Transparency and explainability

- Inform the user about how the recommendation system works
- Ability to understand why an item is recommended
- Option to disable personalized recommendations

## ⚠️ Risk Analysis

### Methodology

**Assessment grid**:
- **Probability**: Low / Medium / High
- **Impact**: Low / Medium / Critical
- **Criticality**: Probability × Impact

**Spectre 7D Checklist**:
1. **Data**: Quality, availability, bias
2. **Development**: Skills, technologies
3. **Deployment**: Infrastructure, scalability
4. **Dependability**: Reliability, performance
5. **Detectability**: Monitoring, alerts
6. **Diversity**: Inclusion, fairness
7. **Documentation**: Traceability, compliance

### Main Identified Risks

**Technical risks**:
- Insufficient performance of the recommendation model
- Latency too high in production
- Scalability difficulties under increased load

**Organizational risks**:
- Unavailability of resources (Data Scientists)
- Turnover in the project team
- Delays in deliverables

**Financial risks**:
- Azure budget overrun
- Underestimated development costs
- ROI taking longer than expected

**Legal risks**:
- GDPR non-compliance (financial penalties)
- User complaints about data management
- Disputes related to model bias

### Mitigation Plan

For each critical risk:
- **Preventive action**: Measure to reduce probability
- **Corrective action**: Measure if the risk materializes
- **Owner**: Person in charge of follow-up
- **Deadline**: Implementation date

**Examples**:
- **GDPR risk** → Compliance audit with the DPO before each sprint
- **Bias risk** → Dataset diversification and regular testing
- **Scalability risk** → Load testing and elastic cloud architecture

## 🎓 Skills Developed

- End-to-end AI project scoping
- Agile SCRUM methodology
- Estimation and costing of technical projects
- Profitability analysis (ROI, break-even)
- GDPR compliance and personal data management
- Risk identification and mitigation
- Ethical issues in AI (bias, transparency)
- Stakeholder management (COMEX)
- Project communication and presentation

## 👤 Author

**Grégoire Mureau**  
Completion date: 2025

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*This project was carried out as part of the AI Engineer program*