Hi, I' m Mayank
I specialize in optimizing models and deploying
AI-driven solutions for real-world impact.

About
I am a Machine Learning Engineer and Data Scientist My focus lies in designing scalable machine learning models, automating workflows with MLOps, and optimizing systems for production deployment. Currently, I am focused on leveraging generative ai and llms to solve complex challenges while ensuring models are scalable, efficient, and production-ready.
Projects
Check out my work
I've worked on a range of machine learning projects. Here are a few that showcase my expertise and dedication to the field.

Fine-Tuning Large Language Models (LLMs)
Fine-tuned various open-source LLMs including LLaMA 2, Mistral, Qwen, and vision-language models for domain-specific tasks. Leveraged efficient methods like LoRA, QLoRA, and quantization using Unsloth and Hugging Face.

Large Language Model (LLM) from Scratch
Implemented a Large Language Model (LLM) from scratch, covering every stage from data preparation and model architecture to pretraining and fine-tuning. This project demystifies transformer-based models through hands-on code and experiments, enabling a deeper understanding of attention mechanisms and token prediction.

US Visa Approval Prediction using MLOps
Built an end-to-end MLOps pipeline to predict the approval status of US visa applications. Implemented machine learning models, deep learning techniques, and automated deployment pipelines.

Interactive ML Algorithm Visualizer
An interactive web application designed to help users understand and experiment with machine learning algorithms visually. It offers dynamic visualizations for concepts like linear regression, allowing users to adjust parameters such as slope and intercept interactively. The platform aims to simplify ML concepts for learners through intuitive design and engaging tools.

Network Security - Malicious URL Detection using MLOps
Developed an end-to-end MLOps project to detect malicious URLs using XGBoost. Integrated robust pipelines for data ingestion, model training, deployment, and monitoring.

Customer Satisfaction Prediction using ZenML
Predicted customer satisfaction scores for future orders using historical e-commerce data from the Brazilian E-Commerce Public Dataset by Olist. This project leverages multiple machine learning models like CatBoost, XGBoost, and LightGBM, built within a ZenML pipeline to create a production-ready solution.
Latest Blogs
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Some of My Lectures

A visual introduction to tokenization in LLMs | Byte Pair Encoding Algorithm
March 13, 2025
In this video, I have explained tokenization in Large Language Models (LLMs) in a visual manner.
Skills
Get in Touch
Want to chat? Just shoot me a dm with a direct question on twitter and I'll respond whenever I can. I will ignore all soliciting.