# 🗺️ Python ML/AI Project Development Roadmap

**Project Type**: Machine Learning / AI Application
**Primary Language**: Python 3.11+
**Focus**: Model training, deployment, and monitoring
**Framework Version**: VERSATIL SDLC v6.4.0

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## 🤖 Recommended OPERA Agents

### Critical Agents (Primary Development)

- **Dr.AI-ML** `.claude/agents/dr-ai-ml.md`
  Machine learning architecture, model training, MLOps best practices

- **Marcus-Backend** `.claude/agents/marcus-backend.md`
  API architecture for model serving, infrastructure, security

- **Marcus-Python** `.claude/agents/sub-agents/marcus-backend/marcus-python-backend.md`
  Python async patterns, FastAPI/Flask optimization, PEP compliance

- **Maria-QA** `.claude/agents/maria-qa.md`
  Model validation, testing strategy, quality assurance

### Recommended Agents (Enhanced Workflow)

- **Alex-BA** `.claude/agents/alex-ba.md`
  Requirements analysis, success metrics, use case definition

- **Sarah-PM** `.claude/agents/sarah-pm.md`
  Project coordination, experiment tracking, documentation

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## 📅 4-Week Development Plan

### Week 1: Data Pipeline & Experimentation Setup

**Description**: Establish data infrastructure, experiment tracking, and baseline model

**Primary Agents**: Alex-BA, Dr.AI-ML, Marcus-Python

**Tasks**:
- [ ] Requirements analysis with Alex-BA:
  - Define ML problem (classification, regression, clustering, etc.)
  - Identify success metrics (accuracy, F1, precision, recall, etc.)
  - Define business constraints (latency, cost, interpretability)
- [ ] Set up Python environment:
  - Poetry or conda for dependency management
  - Virtual environment with Python 3.11+
  - GPU/TPU configuration (if applicable)
- [ ] Data infrastructure:
  - Data ingestion pipeline (batch or streaming)
  - Data versioning (DVC or similar)
  - Data quality validation (Great Expectations)
  - Exploratory Data Analysis (EDA) notebooks
- [ ] Set up experiment tracking:
  - MLflow, Weights & Biases, or Neptune
  - Track hyperparameters, metrics, artifacts
  - Model registry for versioning
- [ ] Establish baseline model:
  - Simple model for performance baseline
  - Document baseline metrics
  - Identify improvement opportunities

**Quality Gates**:
- ✅ Data pipeline reproducible
- ✅ Experiment tracking operational
- ✅ Baseline model metrics documented
- ✅ Data quality checks passing
- ✅ EDA insights documented

**Estimated Effort**: 40-50 hours

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### Week 2: Feature Engineering & Model Development

**Description**: Feature engineering, model training, hyperparameter tuning

**Primary Agents**: Dr.AI-ML, Marcus-Python, Maria-QA

**Feature Engineering**:
- [ ] Feature extraction:
  - Domain-specific feature engineering
  - Text: TF-IDF, embeddings (Word2Vec, BERT)
  - Images: CNN feature extraction
  - Time series: lag features, rolling statistics
- [ ] Feature selection:
  - Correlation analysis
  - Feature importance (SHAP, permutation importance)
  - Dimensionality reduction (PCA, t-SNE, UMAP)
- [ ] Feature validation:
  - Train/validation/test split
  - Cross-validation strategy
  - Data leakage prevention
- [ ] Feature pipeline:
  - Scikit-learn Pipeline or custom
  - Feature scaling/normalization
  - Categorical encoding (one-hot, target, embedding)

**Model Development**:
- [ ] Model selection:
  - Compare multiple algorithms (XGBoost, LightGBM, Random Forest, etc.)
  - Ensemble methods (stacking, bagging, boosting)
  - Deep learning (PyTorch, TensorFlow/Keras) if applicable
- [ ] Hyperparameter tuning:
  - Grid search, random search, or Bayesian optimization (Optuna)
  - Track experiments with MLflow/W&B
  - Early stopping and regularization
- [ ] Model evaluation:
  - Metrics: accuracy, precision, recall, F1, AUC-ROC
  - Confusion matrix and classification report
  - Cross-validation scores
  - Model explainability (SHAP values)

**Testing**:
- [ ] Unit tests for feature engineering functions
- [ ] Data validation tests (schema, distribution)
- [ ] Model smoke tests (inference on sample data)
- [ ] Test coverage >= 70% (ML code can be harder to test)

**Quality Gates**:
- ✅ Model performance exceeds baseline by >= 10%
- ✅ Feature engineering pipeline reproducible
- ✅ Hyperparameter tuning logged in experiment tracker
- ✅ Model explainability analysis complete
- ✅ Unit tests passing with >= 70% coverage

**Estimated Effort**: 60-80 hours

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### Week 3: Model Optimization & Serving API

**Description**: Model optimization, deployment pipeline, API development

**Primary Agents**: Dr.AI-ML, Marcus-Python, Maria-QA

**Model Optimization**:
- [ ] Model compression:
  - Quantization (reduce model size)
  - Pruning (remove unnecessary weights)
  - Knowledge distillation (smaller student model)
- [ ] Inference optimization:
  - Batch prediction
  - ONNX conversion for faster inference
  - GPU/TPU optimization (if applicable)
  - Model caching strategies
- [ ] Latency optimization:
  - Profile inference code (cProfile, py-spy)
  - Optimize preprocessing pipeline
  - Target: < 100ms for real-time, < 1s for batch
- [ ] Model validation:
  - Test on holdout test set
  - Validate on edge cases
  - Check for bias and fairness

**Model Serving API**:
- [ ] API framework setup:
  - FastAPI (recommended) or Flask
  - Pydantic models for request/response validation
  - Async endpoints for non-blocking inference
- [ ] Inference endpoints:
  - `/predict` - Single prediction
  - `/batch-predict` - Batch predictions
  - `/health` - Health check
  - `/metrics` - Prometheus metrics
- [ ] Model loading strategy:
  - Lazy loading vs. pre-loading
  - Model versioning in API
  - Hot-swapping models without downtime
- [ ] Error handling:
  - Input validation errors
  - Model prediction failures
  - Logging and monitoring
- [ ] API documentation:
  - OpenAPI/Swagger auto-generated
  - Request/response examples
  - Error codes and troubleshooting

**Integration Testing**:
- [ ] API integration tests (pytest + httpx)
- [ ] Load testing (Locust) - target throughput
- [ ] Model performance regression tests
- [ ] Edge case testing (empty inputs, outliers)

**Quality Gates**:
- ✅ Inference latency meets requirements
- ✅ API endpoints validated and documented
- ✅ Load testing passed (target throughput achieved)
- ✅ Model performance on test set validated
- ✅ Security scan passed (Bandit for Python)

**Estimated Effort**: 60-80 hours

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### Week 4: MLOps, Monitoring & Deployment

**Description**: Production deployment, monitoring, and MLOps pipeline

**Primary Agents**: Sarah-PM, Dr.AI-ML, Marcus-Python, Maria-QA

**MLOps Pipeline**:
- [ ] CI/CD for ML:
  - Automated training pipeline (Airflow, Prefect, or Kubeflow)
  - Model retraining triggers (data drift, performance degradation)
  - Automated model validation before deployment
  - Blue-green deployment for zero downtime
- [ ] Model registry:
  - MLflow Model Registry or custom
  - Version control for models
  - Model lineage tracking (data, code, hyperparameters)
  - Rollback capability
- [ ] Model monitoring:
  - Input data monitoring (distribution drift)
  - Prediction monitoring (output distribution)
  - Performance monitoring (accuracy, latency)
  - Data drift detection (KS test, PSI)
- [ ] Alerting and logging:
  - Alerts for data drift, model degradation
  - Structured logging (JSON logs)
  - Log aggregation (ELK stack or Loki)
  - Error tracking (Sentry)

**Production Deployment**:
- [ ] Containerization:
  - Docker container for API
  - Multi-stage build for optimization
  - Health checks and graceful shutdown
- [ ] Deploy to cloud:
  - AWS (SageMaker, Lambda, ECS) or GCP (Vertex AI, Cloud Run)
  - Auto-scaling based on load
  - Load balancer configuration
- [ ] Database for predictions:
  - Store predictions for audit trail
  - PostgreSQL or DynamoDB
  - Data retention policy
- [ ] Monitoring dashboards:
  - Grafana or cloud-native dashboards
  - Model performance metrics
  - System metrics (CPU, memory, latency)
  - Business metrics (throughput, error rate)

**Documentation**:
- [ ] Model card (model architecture, training data, metrics)
- [ ] API documentation (Swagger/OpenAPI)
- [ ] Deployment runbook
- [ ] Model retraining guide
- [ ] Monitoring and alerting guide
- [ ] Troubleshooting guide

**Quality Gates**:
- ✅ CI/CD pipeline operational
- ✅ Model deployed to production
- ✅ Monitoring dashboards active
- ✅ Alerting rules configured
- ✅ Rollback tested
- ✅ Documentation complete
- ✅ Zero critical/high issues

**Estimated Effort**: 50-60 hours

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## 🎯 Quality Strategy

### Testing Approach
- **Unit Tests**: pytest for feature engineering, preprocessing
- **Data Tests**: Great Expectations for data quality
- **Model Tests**: Smoke tests, regression tests for performance
- **Integration Tests**: API endpoint tests with pytest + httpx
- **Load Tests**: Locust for throughput and latency validation

### Code Quality
- **Linting**: Ruff or Black for formatting, mypy for type hints
- **Code Review**: Maria-QA reviews all ML code
- **Test Coverage**: >= 70% (ML code requires domain-specific testing)
- **Documentation**: Docstrings for all functions (NumPy style)

### Model Quality
- **Performance Metrics**: Track accuracy, precision, recall, F1, AUC-ROC
- **Explainability**: SHAP values or LIME for model interpretation
- **Bias Detection**: Fairness metrics (demographic parity, equalized odds)
- **Robustness**: Test on adversarial examples and edge cases

### MLOps Standards
- **Experiment Tracking**: MLflow or W&B for all experiments
- **Model Versioning**: Semantic versioning for models
- **Data Versioning**: DVC or similar for reproducibility
- **Monitoring**: Data drift, concept drift, performance degradation

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## 🚀 Deployment Checklist

### Pre-Deployment
- ✅ Model validated on test set (metrics meet requirements)
- ✅ Model explainability analysis complete
- ✅ Bias and fairness checks passed
- ✅ API integration tests passing
- ✅ Load testing passed (target throughput achieved)
- ✅ Security scan passed (Bandit)
- ✅ Model card documented

### Infrastructure
- ✅ Docker image built and tested
- ✅ Environment variables configured
- ✅ GPU/TPU resources provisioned (if needed)
- ✅ Auto-scaling configured
- ✅ Load balancer configured
- ✅ Database for predictions set up

### Monitoring & Observability
- ✅ Model performance monitoring active
- ✅ Data drift detection enabled
- ✅ Alerting rules configured
- ✅ Logging aggregation enabled
- ✅ Dashboards operational (Grafana or cloud-native)
- ✅ Error tracking enabled (Sentry)

### MLOps
- ✅ Model registered in model registry
- ✅ CI/CD pipeline operational
- ✅ Retraining triggers configured
- ✅ Rollback procedure tested
- ✅ Data lineage documented

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## 💡 Technology Stack Recommendations

### Core ML Frameworks
- **Scikit-learn**: Classical ML algorithms
- **XGBoost / LightGBM**: Gradient boosting
- **PyTorch**: Deep learning (recommended for research)
- **TensorFlow / Keras**: Deep learning (production-ready)
- **Hugging Face Transformers**: NLP models

### Data & Experimentation
- **Pandas / Polars**: Data manipulation
- **NumPy**: Numerical computing
- **DVC**: Data version control
- **MLflow / W&B**: Experiment tracking and model registry
- **Great Expectations**: Data quality validation

### Model Serving
- **FastAPI**: API framework (recommended)
- **Flask**: Lightweight API alternative
- **BentoML**: ML model serving framework
- **TorchServe / TensorFlow Serving**: Framework-specific serving
- **ONNX Runtime**: Cross-framework inference

### MLOps & Deployment
- **Docker**: Containerization
- **Kubernetes**: Orchestration (for large-scale)
- **AWS SageMaker / GCP Vertex AI**: Managed ML platforms
- **Airflow / Prefect**: Workflow orchestration
- **Prometheus + Grafana**: Monitoring

### Testing & Quality
- **pytest**: Unit and integration tests
- **Great Expectations**: Data validation
- **SHAP / LIME**: Model explainability
- **Locust**: Load testing
- **Bandit**: Security scanning

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## 📊 Success Metrics

### Model Performance
- **Accuracy / F1 / AUC-ROC**: Meet business requirements
- **Inference Latency**: < 100ms (real-time), < 1s (batch)
- **Throughput**: Meet expected request volume
- **Model Drift**: < 5% performance degradation over time

### MLOps Metrics
- **Experiment Tracking**: 100% of experiments logged
- **Model Deployment Time**: < 30 minutes from training to production
- **Rollback Time**: < 5 minutes in case of issues
- **Data Quality**: 100% of data validated before training

### Business Metrics
- **Model Uptime**: >= 99.9%
- **False Positive Rate**: Meets business tolerance
- **Cost per Prediction**: Within budget
- **User Satisfaction**: Feedback on model predictions

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## 🔗 Related Resources

- **Scikit-learn Documentation**: [scikit-learn.org](https://scikit-learn.org)
- **PyTorch Documentation**: [pytorch.org/docs](https://pytorch.org/docs)
- **MLflow**: [mlflow.org](https://mlflow.org)
- **Weights & Biases**: [wandb.ai](https://wandb.ai)
- **FastAPI**: [fastapi.tiangolo.com](https://fastapi.tiangolo.com)
- **VERSATIL Framework**: `.claude/AGENTS.md`, `.claude/rules/README.md`

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**🤖 Generated by VERSATIL SDLC Framework v6.4.0**
**Last Updated**: ${new Date().toISOString()}
