Python for MLOps - Educational Course

Comprehensive Python programming course designed specifically for MLOps engineers and practitioners.

🎯 Course Overview

This repository contains practical Python examples and exercises covering essential programming concepts for Machine Learning Operations (MLOps). Each module builds upon previous knowledge to create a solid foundation for MLOps development.

Course URL: https://nubes.academy/python-for-mlops-mlops-specialization/

📚 Course Structure

Core Python Fundamentals

• 01_core_python_basics/ - Values, types, strings, collections, naming conventions
• 02_control_flow_and_logic/ - Operators, conditionals, loops, comprehensions
• 03_functions/ - Function definition, lambda functions
• 04_typing_and_linting/ - Type hints and code quality

Advanced Python Concepts

• 05_text_processing/ - Pattern matching, regular expressions
• 06_modules_and_packages/ - Code organization, imports, third-party packages
• 07_classes_and_oop/ - Object-oriented programming concepts
• 08_exceptions_and_error_handling/ - Robust error handling patterns

System Integration

• 09_file_and_os_operations/ - File I/O, paths, environment variables, subprocess
• 10_logging/ - Structured logging for production systems
• 11_serialization/ - Data persistence with JSON, YAML, Pickle

Quality Assurance

• 12_testing/ - Comprehensive testing strategies with pytest and unittest

Data Processing

• 13_numeric_data_with_numpy/ - Numerical computing fundamentals
• 14_data_analysis_with_pandas/ - Data manipulation and analysis
• 15_external_libraries/ - Configuration management and HTTP clients

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Basic programming knowledge

Installation

# Clone the repository
git clone <repository-url>
cd python-for-mlops

# Install dependencies
pip install -r requirements.txt

Running Examples

# Navigate to any module
cd 01_core_python_basics

# Run individual examples
python 01_values_and_types.py

# Run tests (where available)
pytest

🎓 Learning Path

1. Start with Core Basics - Master Python fundamentals
2. Practice Control Flow - Understand program logic and iteration
3. Learn Functions - Write reusable, modular code
4. Add Type Safety - Implement type hints and linting
5. Process Text Data - Handle string operations and patterns
6. Organize Code - Structure projects with modules and packages
7. Apply OOP - Use classes for complex data structures
8. Handle Errors - Implement robust error handling
9. Integrate Systems - Work with files, OS, and external processes
10. Add Logging - Implement production-ready logging
11. Persist Data - Serialize and deserialize data structures
12. Test Everything - Write comprehensive tests
13. Process Numbers - Use NumPy for numerical computing
14. Analyze Data - Leverage Pandas for data manipulation
15. Use Libraries - Integrate external tools and services

🛠 MLOps Focus Areas

This course emphasizes Python skills specifically relevant to MLOps:

• Data Pipeline Development - Processing and transforming ML datasets
• Model Deployment - Serving models in production environments
• Configuration Management - Managing ML experiment parameters
• Error Handling - Building robust ML systems
• Testing - Ensuring ML code quality and reliability
• Monitoring - Logging and observability for ML systems
• API Integration - Connecting ML services and data sources

📖 Additional Resources

• Course Website: nubes.academy/python-for-mlops-mlops-specialization
• Python Documentation: docs.python.org
• MLOps Best Practices: Industry-standard patterns and practices

🤝 Contributing

This is an educational repository. For course-related questions or feedback, please refer to the course platform.

📄 License

Educational content for the MLOps Specialization Course.

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Happy Learning! 🐍✨