I'm an ML Engineer with 6 years of experience in R&D: Computer Vision and NLP.
Currently focusing on model optimization and building scalable ML / MLOps pipelines.
Learn more about my approach to AI and work experience on my cute little website.

• Simplify the complex
  Aim to explain challenging concepts in plain language, avoiding the dense jargon often found in academic papers.

• Avoid unnecessary work
  Take the time to assess whether it's truly worth it and how to approach it in the most efficient way possible.

• Organize for success
  Time spent organizing and maintaining clean code and workflows pays off not just in the long term but immediately.

• Respect technological boundaries
  Strive to recognize the limits of technology and avoid playing the role of omnipotent creator.

• Stay humble
  A little less ego goes a long way in achieving better results.

• Somatic Marker Hypothesis by Antonio Damasio
• LLM Inference Optimization
• PEFT Method Overview [implementing Adapters in PyTorch]
• Physical Symbol Systems and the Language of Thought
• Building a Transformer (Cross-Attention and MHA Explained)

• Python, C++, Wolfram, LaTex, Git

• NumPy, Pandas, Matplotlib, Plotly

• PyTorch, Lightning, Huggitng Face libs, OpenCV

• MlFlow, DVC, Weights & Biases, Hydra, Optuna, Prometheus, Grafana

• Flask, FastAPI, Docker, CI/CD, AWS SageMaker, Gradio, Streamlit, vLLM

• Transformer Architectures Course.
  Deep exploration of transformer-based architectures such as BERT, GPT, T5, and others.