Lay Sheth

Final year student merging Rust’s performance with Python’s versatility. Specializing in machine learning, distributed systems, and high-performance computing with a passion for optimizing ML workflows through language synergy.

Technical Expertise

I work extensively with Rust and Python ecosystems, focusing on:

Machine learning frameworks (TensorFlow, PyTorch)
Computer vision applications (OpenCV)
Data processing (Polars, Kafka)
Database technologies (SurrealDB)
Distributed systems architecture

Notable Projects

Real-time Pet Classifier

Distributed image classification system using Kafka and CNN
Built with TensorFlow, OpenCV, and Apache Kafka
Features real-time processing with scalable architecture[4]

SurrealDB ML Toolkit

Integration of SurrealDB with machine learning workflows
Developed using Rust, Python, PyTorch, and SurrealDB
Streamlines data operations and model training processes[4]

Dual Amazon Scraper

High-performance web scraping tools for Amazon India
Implemented in both Python and Rust for comparative performance
Optimized for speed and large-scale data extraction[4]

Neural Network Comparison (WIP)

Benchmarking neural network implementations across frameworks
Using Rust, PyTorch, and Keras
Focused on performance analysis and optimization[4]

Current Focus

Exploring deeper integration between Rust and Python for ML applications
Developing high-performance computing solutions for machine learning
Building distributed systems that leverage both languages’ strengths
Working with SurrealDB for next-generation data storage solutions[5]

I’m always open to collaborating on innovative projects that push the boundaries of what’s possible with Rust and Python in machine learning environments.

Connect with me: GitHub | LinkedIn | Email