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Product Requirements Document: Diamond Price Predictor System

Executive Summary

Project: End-to-end diamond price prediction ML platform
Objective: Build production-ready system achieving 95%+ price prediction accuracy
Timeline: 9-month development cycle (MVP → Production → Launch)
Target Market: Diamond retailers, appraisers, wholesalers, and online marketplaces

Core Value Proposition

Automated diamond pricing with enterprise-grade MLOps infrastructure, reducing manual pricing time by 90% while maintaining 95%+ accuracy through advanced XGBoost modeling.

1. Product Overview

System Architecture

Core Components:

  1. ML Pipeline: XGBoost model with 9-feature input (carat, cut, color, clarity, depth, table, x, y, z)
  2. Flask REST API: Production API with /predict and /predict/batch endpoints
  3. Streamlit Dashboard: Interactive web interface for manual predictions
  4. MLOps Infrastructure: MLflow tracking, DVC versioning, Airflow orchestration

Target Accuracy & Performance

  • Model Accuracy: 95%+ on diamond price predictions
  • API Response Time: <200ms for single predictions
  • Throughput: 1000+ requests/minute
  • System Uptime: 99.9% availability

2. Technical Requirements

Technology Stack

ML & Data:

  • Python 3.8+, scikit-learn, XGBoost, pandas, numpy
  • MLflow for experiment tracking and model registry
  • DVC for data and model versioning
  • Apache Airflow for pipeline orchestration

Backend & API:

  • Flask framework for REST API
  • Streamlit for interactive dashboard
  • Docker containerization
  • WSGI server for production deployment

Infrastructure:

  • Docker Compose for multi-service orchestration
  • Cloud storage (S3/GCS) for artifact storage
  • Monitoring with Prometheus/Grafana
  • CI/CD pipeline integration

Data Requirements

Input Features (9 characteristics):

  • carat: Diamond weight (0.1-10.0 range)
  • cut: Quality grade (Fair, Good, Very Good, Premium, Ideal)
  • color: Color grade (D, E, F, G, H, I, J)
  • clarity: Clarity grade (FL, IF, VVS1, VVS2, VS1, VS2, SI1, SI2, I1)
  • depth: Depth percentage (40-80% range)
  • table: Table percentage (40-100% range)
  • x: Length dimension (0-20mm range)
  • y: Width dimension (0-20mm range)
  • z: Height dimension (0-20mm range)

Target Output: Price prediction in USD with confidence intervals

3. User Requirements & API Specification

User Types

  1. Diamond Retailers: Need quick pricing for customer inquiries via web interface
  2. Wholesalers: Require bulk pricing via API integration for high-volume processing
  3. Appraisers: Use both interfaces for validation and client services
  4. Platform Developers: Integrate API into existing marketplace systems

API Endpoints

POST /predict

Single Diamond Price Prediction

{
  "carat": 1.0,
  "cut": "Ideal",
  "color": "F",
  "clarity": "VS1",
  "depth": 62.0,
  "table": 57.0,
  "x": 6.0,
  "y": 6.1,
  "z": 3.8
}

Response: Price prediction with confidence interval (<200ms)

POST /predict/batch

Bulk Diamond Processing

  • Accept CSV uploads with multiple diamonds
  • Process 1000+ diamonds in <60 seconds
  • Return comprehensive results with individual predictions

GET /health

System Health Check

  • API availability and response time
  • Model loading status
  • System resource utilization

GET /model/info

Model Metadata

  • Current model version and accuracy
  • Training date and performance metrics
  • Feature importance and model details

4. Implementation Architecture

Development Structure

project/
├── src/
│   ├── components/
│   │   ├── data_ingestion.py      # Data loading and validation
│   │   ├── data_transformation.py # Feature preprocessing
│   │   ├── model_trainer.py       # XGBoost training pipeline
│   │   └── model_evaluation.py    # Performance assessment
│   └── pipeline/
│       └── prediction_pipeline.py # Inference pipeline
├── app.py                         # Flask API server
├── streamlit_app.py               # Dashboard interface
├── requirements.txt               # Python dependencies
├── Dockerfile.flask               # API containerization
├── docker-compose.yaml            # Multi-service setup
├── dvc.yaml                       # DVC pipeline config
├── params.yaml                    # Hyperparameters
└── airflow/dags/                  # Airflow workflows

MLOps Pipeline

Training Pipeline:

  1. Data ingestion with quality validation
  2. Feature engineering and preprocessing
  3. XGBoost model training with hyperparameter optimization
  4. Model evaluation and performance validation
  5. Model registration in MLflow registry
  6. Automated deployment to production

Inference Pipeline:

  1. Input validation and preprocessing
  2. Model loading from MLflow registry
  3. Prediction generation with confidence intervals
  4. Response formatting and logging

Monitoring & Retraining:

  • Automated model performance monitoring
  • Data drift detection and alerting
  • Scheduled retraining workflows
  • A/B testing for model improvements

5. Performance Requirements & Success Criteria

Technical Performance Targets

Model Performance:

  • Prediction accuracy: 95%+ on test dataset
  • Training time: <10 minutes on standard hardware
  • Model size: <100MB for efficient deployment
  • Feature processing: <10ms per prediction

API Performance:

  • Single prediction: <200ms response time
  • Batch processing: 1000+ diamonds in <60 seconds
  • Throughput: 1000+ requests/minute
  • Concurrent users: 100+ simultaneous connections

System Performance:

  • Uptime: 99.9% availability SLA
  • Container startup: <30 seconds
  • Memory usage: <2GB per API instance
  • CPU utilization: <70% under normal load

Quality Assurance Standards

Code Quality:

  • Test coverage: 80%+ across all components
  • Code formatting: Black, isort for Python
  • Linting: flake8, pylint compliance
  • Type checking: mypy for static analysis

Security Standards:

  • API authentication: JWT or API key-based
  • Input validation: Comprehensive parameter checking
  • Rate limiting: Configurable request throttling
  • Error handling: No sensitive data in error messages

Monitoring Requirements:

  • Model performance: Accuracy tracking and drift detection
  • API metrics: Response times, error rates, throughput
  • System health: CPU, memory, disk usage monitoring
  • Business metrics: Prediction volume and user engagement

6. Development Roadmap

Phase 1: MVP Development (Months 1-3)

Month 1: Core ML Pipeline

  • Data ingestion and preprocessing implementation
  • XGBoost model training with hyperparameter optimization
  • Model evaluation framework and performance validation
  • MLflow experiment tracking setup

Month 2: API Development

  • Flask REST API implementation with core endpoints
  • Input validation and comprehensive error handling
  • Model serving infrastructure and caching
  • Docker containerization for deployment

Month 3: User Interface

  • Streamlit dashboard with interactive forms
  • Batch processing functionality for CSV uploads
  • Visualization components and user experience testing
  • Integration testing and performance optimization

Phase 2: Production Readiness (Months 4-6)

Month 4: MLOps Infrastructure

  • Apache Airflow pipeline orchestration
  • DVC data and model versioning
  • Automated monitoring and drift detection
  • Comprehensive testing pipeline (unit, integration, e2e)

Month 5: Security & Scalability

  • Authentication and authorization system
  • Rate limiting and security hardening
  • Horizontal scaling with load balancing
  • Security audit and penetration testing

Month 6: Quality Assurance

  • Performance optimization and load testing
  • Documentation completion (API specs, user guides)
  • Beta testing program with select users
  • Production deployment preparation

Phase 3: Launch & Growth (Months 7-9)

Months 7-8: Beta Launch

  • Private beta with 50+ industry professionals
  • Feedback collection and product refinement
  • Customer onboarding process optimization
  • Performance monitoring and issue resolution

Month 9: Public Launch

  • Public product announcement and marketing
  • Customer acquisition automation
  • Partnership development with industry platforms
  • Revenue optimization and growth metrics tracking

7. Success Metrics & Key Performance Indicators

Technical Success Metrics

Model Performance:

  • Prediction accuracy: 95%+ on test dataset
  • Mean Absolute Error: <$500 for diamonds $1K-$10K range
  • R² score: >0.90 for price correlation
  • Training stability: <5% variance across training runs

API Performance:

  • Response time: <200ms for 95% of requests
  • Throughput: 1000+ requests/minute sustained
  • Error rate: <1% across all endpoints
  • Uptime: 99.9% availability (43 minutes downtime/month max)

System Quality:

  • Test coverage: 80%+ across all components
  • Code quality: 90%+ linting score
  • Security: Zero critical vulnerabilities
  • Documentation: 100% API endpoint coverage

Business Success Metrics

User Adoption (6-month targets):

  • Monthly active users: 200+ (retailers, appraisers, developers)
  • API calls: 50,000+ monthly requests
  • User retention: 60%+ month-over-month
  • Feature adoption: 40%+ using batch processing

Market Impact:

  • Time savings: 90%+ vs manual pricing methods
  • Customer satisfaction: NPS >50 from user surveys
  • Industry partnerships: 3+ integration agreements
  • Use case coverage: All 4 primary user types active

Monitoring & Analytics Framework

Real-time Monitoring:

  • System health dashboard (Grafana/similar)
  • API performance metrics and alerting
  • Model accuracy tracking and drift detection
  • User activity and engagement analytics

Reporting Schedule:

  • Daily: System performance and error monitoring
  • Weekly: User engagement and feature usage analysis
  • Monthly: Business metrics and customer satisfaction review
  • Quarterly: Strategic goal assessment and roadmap updates

8. Risk Assessment & Mitigation Strategies

Primary Risks & Mitigation

Technical Risks:

  • Model accuracy below 95%: Implement ensemble methods, expand training data
  • API performance issues: Load testing, caching optimization, horizontal scaling
  • System reliability problems: Comprehensive monitoring, automated failover, testing

Business Risks:

  • Low user adoption: Early user research, beta testing, iterative improvement
  • Market competition: Focus on technical excellence and user experience
  • Integration challenges: Comprehensive API documentation, developer support

Operational Risks:

  • Team capacity constraints: Prioritize P0 features, maintain technical debt
  • Timeline delays: Agile development, regular checkpoint reviews
  • Quality issues: Automated testing, code review processes, staging environment

9. Definition of Done & Acceptance Criteria

Phase 1 MVP Completion Criteria

  • ✅ Model achieves 95%+ accuracy on test dataset
  • ✅ API responds <200ms for single predictions
  • ✅ Streamlit dashboard fully functional for manual predictions
  • ✅ Docker deployment pipeline operational
  • ✅ MLflow tracking and model registry configured

Phase 2 Production Readiness Criteria

  • ✅ System maintains 99.9% uptime under load
  • ✅ Supports 100+ concurrent users
  • ✅ Complete MLOps pipeline with automated retraining
  • ✅ Security audit passed with authentication system
  • ✅ Comprehensive monitoring and alerting operational

Phase 3 Launch Success Criteria

  • ✅ 50+ active beta users providing feedback
  • ✅ 25,000+ monthly API calls sustained
  • ✅ NPS score >40 from user surveys
  • ✅ 2+ industry partnership agreements signed
  • ✅ Public documentation and developer resources complete

Document Control

Version: 2.0 (Focused Implementation)
Last Updated: January 2025
Next Review: Monthly during development phases
Stakeholders: ML Engineering, Product Management, Business Development

This streamlined PRD focuses on the core technical implementation and measurable success criteria for building a production-ready diamond price prediction system with enterprise-grade MLOps infrastructure.