feat: modernize project infrastructure for cookiecutter data science compliance

- SIMPLIFY: Replace 3-service Docker setup with single IRIS container
- MODERNIZE: Replace setup.py with pyproject.toml for modern Python packaging
- AUTOMATE: Add comprehensive Makefile for development workflow
- STANDARDIZE: Add .clinerules-data-scientist for project-specific best practices
- OPTIMIZE: Implement uv support with pip fallback for faster dependency management
- STREAMLINE: Update README with simplified quick start (make setup && make notebooks)

This dramatically reduces complexity while preserving all 5 educational demos and
IRIS IntegratedML integration value. Follows 'bare minimum' principle for easy
developer adoption while maintaining data science best practices.

Author: tom-dyar

.clinerules-data-scientist

@@ -0,0 +1,42 @@
+# Data Science Project Rules for IntegratedML
+# Following cookiecutter data science best practices + modern tooling
+
+## Dependency Management
+- Use `uv` for fast, reliable package management when possible
+- Pin exact versions in requirements.txt for reproducibility
+- Keep dependencies minimal - avoid bloat
+
+## Code Organization
+- Heavy logic in .py modules under shared/ and demos/
+- Keep notebooks light - use for exploration and presentation
+- Use type hints and docstrings throughout
+
+## Reproducibility
+- Log all preprocessing steps and data transformations
+- Save model artifacts with versioning information
+- Include confidence intervals in model benchmarks
+- Use DVC for large data/model versioning (when applicable)
+
+## Development Workflow
+- Use Makefile for common tasks and automation
+- IRIS database via Docker, Python development locally
+- VS Code for notebook development (not Jupyter server)
+- Test-driven development with pytest
+
+## Data Hygiene
+- Validate inputs at module boundaries
+- Document data assumptions and limitations
+- Keep raw data immutable
+- Track data lineage and transformations
+
+## Model Development
+- Benchmark against baseline models
+- Report robust metrics with confidence intervals
+- Embed key plots and link to saved artifacts
+- Optimize for readability over cleverness
+
+## Notebook Guidelines
+- Clear all outputs before committing
+- Use markdown for explanations and context
+- Import shared utilities rather than duplicating code
+- Include executive summary and next steps

Makefile

@@ -0,0 +1,143 @@
+# IntegratedML Flexible Model Integration - Development Automation
+# Simplified workflow for IRIS + local development
+
+.PHONY: help setup start stop clean install test format lint notebooks
+
+# Default target
+help:
+	@echo "IntegratedML Flexible Model Integration - Available Commands:"
+	@echo ""
+	@echo "Setup & Environment:"
+	@echo "  setup     - Complete project setup (dependencies + IRIS)"
+	@echo "  install   - Install Python dependencies only"
+	@echo "  start     - Start IRIS database"
+	@echo "  stop      - Stop IRIS database"
+	@echo "  clean     - Clean up containers and volumes"
+	@echo ""
+	@echo "Development:"
+	@echo "  notebooks - Open notebooks directory in VS Code"
+	@echo "  test      - Run all tests"
+	@echo "  format    - Format code with black"
+	@echo "  lint      - Run linting checks"
+	@echo ""
+	@echo "Quick Start:"
+	@echo "  make setup && make notebooks"
+
+# Environment setup
+setup: install start
+	@echo "✅ Setup complete! Run 'make notebooks' to start exploring."
+
+install:
+	@echo "📦 Installing Python dependencies with uv..."
+	@if command -v uv >/dev/null 2>&1; then \
+		uv sync; \
+	else \
+		echo "⚠️  uv not found, falling back to pip..."; \
+		pip install -r requirements.txt; \
+	fi
+	@echo "✅ Dependencies installed"
+
+install-uv:
+	@echo "🚀 Installing uv (ultra-fast Python package manager)..."
+	curl -LsSf https://astral.sh/uv/install.sh | sh
+	@echo "✅ uv installed! Restart your shell or run: source ~/.cargo/env"
+
+# IRIS database management
+start:
+	@echo "🚀 Starting IRIS database..."
+	docker-compose up -d iris
+	@echo "⏳ Waiting for IRIS to be ready..."
+	@timeout 120 bash -c 'until docker-compose exec iris iris session iris -U USER "w \"Database ready\""; do sleep 2; done'
+	@echo "✅ IRIS database is ready!"
+	@echo "   Management Portal: http://localhost:52773/csp/sys/UtilHome.csp"
+	@echo "   Database Port: localhost:1972"
+
+stop:
+	@echo "🛑 Stopping IRIS database..."
+	docker-compose stop iris
+	@echo "✅ IRIS stopped"
+
+restart: stop start
+
+# Development tools
+notebooks:
+	@echo "📓 Opening notebooks in VS Code..."
+	code notebooks/ demos/
+
+test:
+	@echo "🧪 Running tests..."
+	@pytest demos/*/tests/ -v --tb=short
+	@echo "✅ Tests completed"
+
+format:
+	@echo "🎨 Formatting code..."
+	black .
+	@echo "✅ Code formatted"
+
+lint:
+	@echo "🔍 Running linting..."
+	flake8 . --max-line-length=88 --extend-ignore=E203,W503
+	mypy shared/ --ignore-missing-imports
+	@echo "✅ Linting completed"
+
+# Cleanup
+clean:
+	@echo "🧹 Cleaning up..."
+	docker-compose down -v
+	docker system prune -f
+	@echo "✅ Cleanup completed"
+
+# Demo-specific targets
+demo-credit:
+	@echo "💳 Running Credit Risk demo..."
+	python run_credit_risk_demo.py
+
+demo-fraud:
+	@echo "🔒 Running Fraud Detection demo..."
+	python run_fraud_detection_demo.py
+
+demo-sales:
+	@echo "📈 Running Sales Forecasting demo..."
+	python run_sales_forecasting_demo.py
+
+demo-dna:
+	@echo "🧬 Running DNA Similarity demo..."
+	python run_dna_similarity_demo.py
+
+# Quick demo runner
+demos: start
+	@echo "🎬 Running all demos..."
+	@$(MAKE) demo-credit
+	@$(MAKE) demo-fraud
+	@$(MAKE) demo-sales
+	@$(MAKE) demo-dna
+	@echo "✅ All demos completed!"
+
+# Status check
+status:
+	@echo "📊 System Status:"
+	@echo ""
+	@docker-compose ps
+	@echo ""
+	@if docker-compose exec iris iris session iris -U USER "w \"IRIS Status: Online\"" 2>/dev/null; then \
+		echo "✅ IRIS: Online"; \
+	else \
+		echo "❌ IRIS: Offline"; \
+	fi
+
+# Development utilities
+dev-setup: install
+	@echo "🛠️  Setting up development environment..."
+	pip install pre-commit
+	pre-commit install
+	@echo "✅ Development tools configured"
+
+# Container logs
+logs:
+	docker-compose logs -f iris
+
+# Database initialization
+init-db: start
+	@echo "🗃️  Initializing database with sample data..."
+	python setup_iris_integratedml.py
+	@echo "✅ Database initialized"

README.md

@@ -19,56 +19,68 @@ A demonstration framework for integrating custom machine learning models with In
 ## Quick Start
 
 ### Prerequisites
-- Docker and Docker Compose
-- At least 8GB RAM for containers
-- 20GB free disk space
+- **Docker & Docker Compose** (for IRIS database)
+- **Python 3.8+** (for local development)
+- **VS Code** (recommended for notebooks)
+- At least 4GB RAM for IRIS container
 
-### Docker Setup
+### 🚀 Simplified Setup
 
 ```bash
 # Clone the repository
 git clone https://github.com/intersystems-community/integratedml-flexible-model-integration.git
 cd integratedml-flexible-model-integration
 
-# Initialize Docker environment
-chmod +x docker/docker-init.sh
-./docker/docker-init.sh
+# Complete setup (dependencies + IRIS database)
+make setup
 
-# Configure environment
-cp .env.example .env
-# Edit .env with your preferred settings
-
-# Start IRIS database and application services
-docker-compose up --build -d
+# Open notebooks in VS Code
+make notebooks
 ```
 
-### Run Demo Examples
+That's it! 🎉
+
+### Manual Setup (Alternative)
 
 ```bash
-# Credit Risk Assessment with IRIS IntegratedML
-python run_credit_risk_demo.py
+# 1. Install Python dependencies
+pip install -r requirements.txt
 
-# Fraud Detection with ensemble models
-python run_fraud_detection_demo.py
+# 2. Configure environment
+cp .env.example .env
 
-# Sales Forecasting with hybrid models
-python run_sales_forecasting_demo.py
+# 3. Start IRIS database only
+docker-compose up -d iris
 
-# DNA Similarity Analysis with sequence classification
-python run_dna_similarity_demo.py
+# 4. Open notebooks in VS Code
+code notebooks/ demos/
 ```
 
-### Alternative: Standalone Installation
+### Available Notebooks
 
-```bash
-# For development without Docker
-pip install -r requirements.txt
-pip install -e .
+Explore these comprehensive demos directly in VS Code:
 
-# Launch interactive notebooks
-jupyter notebook demos/credit_risk/notebooks/credit_risk_demo.ipynb
+- [**📓 Quick Start**](notebooks/Iris_IntegratedML_Quickstart.ipynb) - Get familiar with IRIS IntegratedML
+- [**💳 Credit Risk**](demos/credit_risk/notebooks/01_Credit_Risk_Complete_Demo.ipynb) - Financial risk assessment
+- [**🔒 Fraud Detection**](demos/fraud_detection/notebooks/01_Fraud_Detection_Complete_Demo.ipynb) - Real-time fraud prevention
+- [**📈 Sales Forecasting**](demos/sales_forecasting/notebooks/01_Sales_Forecasting_Complete_Demo.ipynb) - Revenue prediction
+- [**🧬 DNA Similarity**](demos/dna_similarity/notebooks/01_DNA_Similarity_Complete_Demo.ipynb) - Genomic analysis
+- [**📊 Time Series**](demos/time_series_native/notebooks/01_Time_Series_Native_Complete_Demo.ipynb) - Native IRIS time series
+
+### 🛠️ Development Commands
+
+```bash
+make help           # Show all available commands
+make start          # Start IRIS database
+make stop           # Stop IRIS database
+make test           # Run all tests
+make demos          # Run all demo scripts
+make status         # Check system status
 ```
 
+### What's New?
+**Simplified Development Workflow**: No more complex multi-container setup! Just IRIS database + local Python development in VS Code.
+
 ## Demo Examples
 
 The framework includes four demonstration scenarios with varying complexity levels.