CamCoLabs
Python + AI tinkering
A personal lab for curious experiments: small tools, ML prototypes, and the occasional deep dive—shared with code, demos, and write-ups.
What you’ll find here
A hobbyist lab for Python + AI experiments
This is my scratchpad for Python + AI experiments—notes, demos, and the occasional deep-dive write-up when something gets interesting.
End-to-end case studies
Problem framing, approach, results, and what I’d improve next time.
Production-minded ML
Reproducible training, evaluation, and deployment considerations (MLOps-aware).
Clean, testable Python
Readable architecture, typed interfaces where it helps, and pragmatic testing.
Systems thinking
Performance, reliability, and scalability tradeoffs explained in plain language.
Featured projects
A quick scan of recent work. For deeper write-ups, see Project Case Studies.
RAG Knowledge Assistant
LLMs • Retrieval • Evaluation
Forecasting Pipeline
Time Series • Backtesting
Computer Vision Classifier
Deep Learning • Metrics
MLOps Deployment Template
CI/CD • Docker • Monitoring
Data Quality Toolkit
Validation • Profiling
API Performance Benchmarks
Profiling • Load Testing
Start with Projects, then open 1–2 write-ups in Project Case Studies for depth.
Yes—each project includes links to code, setup notes, and the evaluation approach where applicable.
Python, PyTorch, scikit-learn, SQL, Docker, GitHub Actions, and cloud fundamentals (AWS/GCP concepts).
When it fits the project: packaging, CI/CD, model serving, monitoring, and rollback considerations.
Yes—everything here is easy to review online, and most projects include code plus notes to reproduce results.
Start with Projects for quick browsing, then jump into Project Case Studies for the full write-ups. For shorter notes, see the Blog.
Want the short list?
Browse the portfolio, then pick a case study to discuss architecture, evaluation, and tradeoffs.