Exploring developing production ready AI systems, cloud native applications, and scalable infrastructure solutions. Currently focused on applied machine learning, generative AI, and modern cloud architectures.

Cloud & Infrastructure Engineering

• Google Cloud Platform (GCP), Kubernetes orchestration, containerization with Docker
• Building scalable, cloud-native applications and microservices architectures

Business Ineterests & Analytics

• Learning tools such as PowerBI

Technical Content Creation

• Publishing in-depth technical articles on Dev.to
• Sharing insights on ML engineering, cloud architecture, and AI development

PyPI Package | Machine Learning Preprocessing Library

A comprehensive toolkit for explainable machine learning preprocessing, featuring automated handling of missing values, intelligent encoding strategies, detailed preprocessing reports, and comprehensive visualizations. Designed to make ML preprocessing transparent and reproducible.

Key Features:

• Automated preprocessing pipeline with explainability
• Comprehensive missing value analysis and handling
• Advanced encoding strategies for categorical variables
• Detailed preprocessing reports and visualizations

Recent Articles:

• How I Built an Open-Source ML Preprocessing Package (And Published it on PyPI)
  How I built and published mt first Open-Source ML Preprocessing library.

• Beyond Tokens: What LLMs actually Understand
  Deep dive into LLM internals and understanding mechanisms

• How I Ditched LangChain for a custom RAG
  Building custom RAG systems with LangGraph for better control and performance

Open to collaborating on innovative ML projects and technical content creation.