A fully serverless ML-powered course recommendation platform built and deployed in just 4 hours during a JCR (Junior Consultant Round) sprint challenge. The AI core is an ensemble of XGBoost and Random Forest multi-label classifiers — trained on member profiles (Filière, Cellule, Sexe + normalized numerics) to predict course suitability, blended 50/50, filtered at a 0.40 probability threshold to produce up to 3 personalized course recommendations per member while excluding already-taken courses. The entire platform runs on Cloudflare Workers edge compute with Neon Serverless Postgres (HTTP driver), Drizzle ORM for type-safe queries, Hono.js routing, and WebAssembly bcrypt auth — deployed globally with sub-50ms response times and $0 hosting cost.
The JCR challenge required building a functional AI-powered application in 4 hours with specific constraints: it must be deployable without traditional servers, handle concurrent users globally, include authentication, and demonstrate practical AI integration.
- 01
Trained a custom ML ensemble for course recommendation: (1) XGBoost multi-label classifier with randomized search hyperparameter tuning, (2) Random Forest with 400 estimators and parallel processing. The data pipeline one-hot encodes categorical features (Filière, Cellule, Sexe), normalizes and clips numeric features, and applies multi-label binarization on target course labels. Both models are serialized as .joblib files for production serving.
- 02
Designed a 50/50 weighted ensemble prediction pipeline: calculate probability scores from both XGBoost and Random Forest, blend them with configurable weights (W_XGB=0.50), filter out courses the member has already taken, apply a 0.40 probability threshold, and return the top 3 recommendations per member — balancing precision (avoiding bad recommendations) with recall (finding all good ones).
- 03
Chose Cloudflare Workers for true edge computing — code runs in 300+ data centers worldwide, eliminating the cold-start penalty of traditional serverless. Integrated Neon Serverless Postgres via its HTTP driver, which works in Cloudflare's V8 isolate environment where traditional TCP connections aren't possible.
- 04
Used Drizzle ORM for type-safe, compile-time-checked database queries — catching SQL errors at build time instead of runtime, crucial when you only have 4 hours. Implemented bcrypt-based authentication using a WebAssembly-compiled bcrypt module that runs in Cloudflare's edge runtime.
The ML pipeline (Python: XGBoost + Random Forest → .joblib models) trains offline and exports predictions. A single Cloudflare Worker handles all API routes via Hono.js, with Drizzle ORM executing queries against Neon Postgres via @neondatabase/serverless HTTP driver. Auth middleware validates bcrypt-hashed sessions on every request. The recommendation engine serves pre-computed ensemble predictions filtered per-user. The entire platform — login, auth, course recommendations, member profiles — was built and deployed in 4 hours.
Multi-label classification with 50/50 blended probabilities — randomized search tuning, 400 RF estimators, 0.40 threshold, top-3 recommendations
One-hot encoding (Filière, Cellule, Sexe), numeric normalization/clipping, multi-label binarization, course-already-taken filtering
Edge compute runtime in 300+ global data centers — zero cold starts
HTTP driver for edge compatibility in V8 isolates — zero-cold-start database access
Type-safe SQL queries + ultra-lightweight edge web framework
Edge-compatible password hashing compiled to WASM for Cloudflare runtime
Won the 4-hour JCR sprint challenge
XGBoost + Random Forest ensemble trained, tuned, and deployed in the 4-hour window
Global response time from any location via Cloudflare edge
Personalized course recommendations per member with 0.40 probability threshold
Runs on Cloudflare's free tier (100K requests/day)
🥇 1st Place — JCR Sprint Closing Ceremony at INSAT, April 2025
Training a real ML model (XGBoost + Random Forest ensemble) and deploying a full platform with auth in 4 hours required extreme prioritization. The key was splitting work: one person on the ML pipeline (data cleaning → training → prediction export) while the other built the serverless API and frontend in parallel.
The 50/50 ensemble blend with a 0.40 threshold was a pragmatic choice — we didn't have time to optimize weights, but the ensemble still outperformed either model individually on micro-F1. Sometimes 'good enough fast' beats 'perfect never'.
Cloudflare Workers + Neon is the best serverless stack for time-constrained challenges — zero configuration, instant deployment, and no cold starts.
Drizzle ORM's compile-time SQL validation saved us from at least 3 runtime bugs that would have cost 30+ minutes each to debug in a 4-hour sprint.