About Me

I'm Daksh Pathak, a machine learning developer who enjoys turning messy real-world data into reliable, production-ready systems. I spend most of my time building models, shipping them to users, and making sure they actually solve meaningful problems.

My toolkit spans Python, TensorFlow, Seaborn, and the wider data science ecosystem for experimentation and analysis. On the product side, I work with React, Redux, TypeScript, and Tailwind CSS to craft interfaces where complex models feel simple and intuitive to use.

I've also spent a lot of time on the backend and infrastructure: Node.js, Django, and Flask for APIs, Go for performant services, and Docker and Kubernetes to deploy everything in a way that scales cleanly. Under the hood, I'm comfortable moving between Java, C++, and Python depending on what the problem demands.

Recently, I've focused on data analysis projects that involve cleaning, transforming, and structuring raw data for downstream machine learning—everything from feature engineering to building robust preprocessing pipelines. I've also been an active open-source contributor, collaborating on libraries and tools that make it easier for other developers to explore data and deploy ML solutions.