Python

Why Python?

Python is my primary language for machine learning, data engineering, and backend development. Its rich ecosystem, readability, and versatility make it ideal for everything from quick prototypes to production ML systems.

Key Strengths

Machine Learning & Data Science

Python's ML ecosystem is unmatched:

• scikit-learn for classical ML algorithms
• PyTorch for deep learning research and production
• Pandas & NumPy for data manipulation
• Dask for distributed computing
• Matplotlib & Seaborn for visualization

Web Development & APIs

• FastAPI for high-performance APIs
• Django/Flask for full-stack applications
• Pydantic for data validation
• SQLAlchemy for database ORMs

DevOps & Automation

• Boto3 for AWS automation
• Kubernetes Python client for cluster management
• Fabric/Ansible for deployment automation
• pytest for comprehensive testing

My Experience

I've used Python extensively across all my major projects:

ML Engineering at CML Insights

• MLOps pipelines with Kubeflow and MLflow
• Data processing with Dask for large-scale datasets
• Model development with PyTorch and scikit-learn
• Backend services with FastAPI
• AWS automation with Boto3

Research Projects

• Deep learning for weather nowcasting
• Computer vision for astronomical image analysis
• Data pipeline development
• Experiment tracking and analysis

Personal Projects

• Multi-agent LLM systems with LangChain
• AWS security auditing tools
• Embedded ML on Raspberry Pi
• Web scraping and automation

Best Practices I Follow

• Type hints with mypy for type safety
• Virtual environments (venv, conda) for isolation
• Black & isort for consistent formatting
• pytest for unit and integration testing
• Poetry or pip-tools for dependency management
• Docstrings and comprehensive documentation
• Async/await for I/O-bound operations

Advanced Features I Use

• Decorators for cross-cutting concerns
• Context managers for resource management
• Generators for memory-efficient iteration
• Asyncio for concurrent operations
• Multiprocessing for CPU-bound parallelism
• Cython/Numba for performance-critical code

Python in Production

• Docker containers with multi-stage builds
• Gunicorn/Uvicorn for WSGI/ASGI serving
• Monitoring with Prometheus client libraries
• Logging with structured logging (structlog)
• Error tracking with Sentry
• Performance profiling with cProfile and py-spy