Ship a Lightweight ML Microservice for an EdTech Reading App
Overview
What this challenge is about.
Engineer features from 50M reading events, train a classifier, and ship it as a FastAPI microservice. You get a verifiable certificate.
The scenario
The startup (around 60 staff) sells through schools across India and Southeast Asia; sub-second responses are non-negotiable on the lower-end Android devices most students use.
The Brief
What you'll do, and what you'll demonstrate.
Train and deploy a sub-200ms reading-pace anomaly microservice that the mobile team can integrate this sprint.
Earning criteria — what you'll demonstrate
- Engineer per-session features under tight latency constraints
- Compare a simple baseline against a stronger model honestly
- Package and load-test a model as a real microservice
- Write integration docs a downstream team can actually use
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Applied Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Applied Machine Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Serving
Apply model serving to solve real industry problems and demonstrate production-level capability.
- Containerization
Apply containerization to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
MLOps Engineer
Shipping a containerized, load-tested inference service with integration docs is the day-one MLOps engineer work at any product-AI company.
This challenge sharpens
- model-serving
- containerization
- ml-pipelines
Machine Learning Engineer
Closing the loop from model training to serving with a latency budget is core MLE work, especially at mobile-first edtech companies.
This challenge sharpens
- feature-engineering
- model-serving
- ml-pipelines
AI Engineer
AI engineers regularly own the boundary between an ML model and a product team — this challenge practices that boundary end-to-end.
This challenge sharpens
- model-serving
- containerization
- model-evaluation