v1.0.6 release

✨ NEW FEATURES

• 🔍 Project Validation Command: New mlops-project-generator validate command
• 📋 Comprehensive Validation: Checks project structure, configuration, and deployment readiness
• 🎯 Framework-Specific Validation: Validates sklearn, PyTorch, and TensorFlow projects
• 🚀 Deployment Readiness: Validates Docker, FastAPI, and deployment configurations
• 🔬 MLflow Configuration: Validates experiment tracking setup
• 📁 Data Folder Safety: Checks data directory structure and .gitignore files
• 📚 Documentation Validation: Ensures proper documentation exists

🎯 VALIDATION FEATURES

• Smart Framework Detection: Automatically detects ML framework from project files
• Detailed Reporting: Beautiful Rich UI with pass/warn/fail status
• Professional Output: Summary panel, detailed results table, and recommendations
• Flexible Path Support: Validate any project path with --path option
• Exit Codes: Proper exit codes for CI/CD integration

🔧 TECHNICAL IMPROVEMENTS

• Modular Design: Separate validator module for easy extension
• Rich UI Integration: Beautiful terminal output with tables and panels
• Comprehensive Testing: Full test coverage for validation functionality
• Error Handling: Graceful error handling and user feedback

📋 VALIDATION CHECKS

• Project Structure: Required directories (src, configs, data, models, scripts)
• Configuration Files: config.yaml, requirements.txt, Makefile, .gitignore
• Framework Files: Framework-specific files (model.py, train.py, etc.)
• Deployment Files: Dockerfile, FastAPI, docker-compose.yml
• MLflow Setup: mlruns directory, MLflow configuration
• Data Safety: Data directories with proper .gitignore files
• Dependencies: Python packages and ML framework detection
• Documentation: README.md, CHANGELOG.md, docs/ directory