A personal learning repository documenting my journey through Python programming and foundational MLOps concepts. This space serves as a reference and sandbox for experimenting with data handling, environment setup, statistical techniques, and lightweight API development — all essential building blocks for modern machine learning workflows.
- Strengthen Python fluency and best practices
- Explore key libraries like NumPy, pandas, and matplotlib
- Practice testing with
pytest - Understand statistical methods for data quality and outlier detection
- Apply concepts from the MLOps specialization by Duke University on Coursera
- Build and deploy simple APIs using FastAPI and Flask
- Python basics and advanced syntax
- Data manipulation with pandas
- Memory optimization in data workflows
- Data visualization with matplotlib
- Indexing strategies and performance tuning
- Outlier detection using IQR and Z-score
- Debugging workflows with
pdb - Automated testing with
pytest - Environment setup and project structure
- API development with FastAPI
- API development with Flask
- MLOps | Machine Learning Operations Specialization – Duke University
- Official documentation (Python, pandas, NumPy, matplotlib, FastAPI, Flask)
- Blog posts and tutorial notebooks from the data science and ML engineering community
Folder structure:
- notebooks/: Jupyter notebooks for exploring code, visualizations, and experiments
- tests/: Unit tests using
pytest - fastapi/: Lightweight API projects using FastAPI
- webapp/: Lightweight API projects using Flask
All other code and notes are currently organized at the root level for simplicity.
Clone the repo and start experimenting:
git clone https://github.com/VandanaJn/python_essentials_ml_ops.git