No CodeML is a distributed MLOps platform that takes a dataset from raw CSV to a production inference API without writing a line of code. It was built as my final-year capstone at FAST NUCES and is designed around three ideas: do the heavy work where it makes sense, train at cloud scale, and deploy instantly.
Highlights
-
Distributed Ray pipeline — Orchestrated end-to-end ML workflows on Ray.io, using Ray Clusters for parallelized training and Ray Serve for scalable, auto-scaling model serving across distributed nodes.
-
Client-side processing with WebAssembly — Engineered browser-based data-processing kernels in WebAssembly and JavaScript Workers, enabling parallel parsing and feature engineering of gigabyte-scale CSV files on the client at near-native speed.
-
One-click deploy — A deployment system that transitions a trained model into a production API on Ray Clusters in a single click, with high availability and auto-scaling inference.
Workflow
- Data Preprocessing — Upload a dataset, preview it instantly, and apply transformations — all processed client-side via Wasm.
- Model Training — Train at cloud scale on distributed Ray Clusters and inspect results.
- Model Deployment — Promote the trained model to a live, auto-scaling inference endpoint.
Tech Stack
Python · React · Ray.io · WebAssembly (Wasm) · AWS