Computer science student working toward a career in MLOps and cloud-based machine learning — building the infrastructure that takes models from a notebook to production.
- C — data structures and memory management (stacks, linked lists, dynamic allocation)
- Git & GitHub — version control fundamentals
I'm following a staged path rather than jumping straight to frameworks. Each stage ends with a small project.
| Stage | Focus | Status |
|---|---|---|
| 1 | Programming foundations, DSA, Linux, SQL | In progress |
| 2 | Mathematics for ML — linear algebra, calculus, statistics | Planned |
| 3 | Data skills — NumPy, Pandas, feature engineering | Planned |
| 4 | Machine learning — scikit-learn, model evaluation | Planned |
| 5 | Deep learning — PyTorch, CNNs, Transformers | Planned |
| 6 | LLMs & GenAI — Hugging Face, RAG, agents | Planned |
| 7 | Deployment — FastAPI, Docker, AWS | Planned |
| 8 | MLOps — MLflow, Kubernetes, CI/CD | Planned |
- note-editor-undo-redo — undo/redo pattern with two stacks in C
- playlist-manager — doubly linked list playlist manager in C
- student-grade-tracker — grade tracking program in C
Learning in public. Everything here is part of the journey, not a finished portfolio.