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Marcsgarden/README.md

Shane Argilagos

Computer Science graduate and U.S. Army veteran based in Warsaw, Poland, focused on cloud engineering, DevOps, cybersecurity, and applied machine learning.

My work combines cloud infrastructure, reproducible experimentation, automated testing, CI, and transparent technical documentation.

Featured Projects

Cloud AI Benchmarking

Cross-cloud benchmarking of AI and machine-learning workloads across AWS, Azure, GCP, and a local baseline using consistent containerized environments.

CartPole Q-Learning From Scratch

Seeded tabular reinforcement-learning implementation with corrected discretization, deterministic evaluation, regression tests, and complete run artifacts.

MNIST MLP Hyperparameter Study

PyTorch investigation of 243 historical MLP configurations with transparent methodology limitations and corrected reproducibility controls.

Mobile Price Classification

Leakage-safe scikit-learn model comparison using pipeline-contained preprocessing, validation-based selection, tests, and documented dataset-provenance limitations.

Technical Focus

  • Cloud: AWS, Microsoft Azure, Google Cloud
  • Programming: Python, C++
  • Machine learning: PyTorch, scikit-learn
  • Infrastructure and tooling: Docker, GitHub Actions, Git, Linux
  • Areas: cloud engineering, DevOps, cybersecurity, applied ML

Background

  • B.Sc. Computer Science graduate
  • U.S. Army veteran with communications and IT support experience
  • Based in Warsaw, Poland

Contact

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  1. cloud-ai-benchmarking cloud-ai-benchmarking Public

    Reproducible AI/ML/LLM benchmarking across AWS, Azure, Google Cloud Platform, and local systems using Docker, PyTorch, CUDA, and statistical performance analysis.

    Python

  2. mnist-mlp-hyperparameter-study mnist-mlp-hyperparameter-study Public

    PyTorch MLP study of 243 historical MNIST configurations, with transparent methodology limitations and a held-out test evaluation of the selected model.

    Python

  3. mobile-price-classification-ml mobile-price-classification-ml Public

    Leakage-safe scikit-learn comparison of logistic regression, ridge regression, and random forest for mobile-price classification, with transparent dataset provenance limits.

    Python

  4. cartpole-q-learning-from-scratch cartpole-q-learning-from-scratch Public

    Seeded tabular Q-learning for CartPole-v1 with corrected state discretization, deterministic evaluation, and fully recorded experiment artifacts.

    Python