Automate Retraining with a Drift-Triggered MLflow Pipeline
Overview
What this challenge is about.
Build a drift-triggered MLflow retraining pipeline with Airflow and Evidently, then earn your verifiable certificate.
The scenario
The Boston healthtech (around 25 staff, regulated by HIPAA-equivalent data agreements with two hospital networks) is preparing for FDA-style audit and needs a documented, repeatable retraining process.
The Brief
What you'll do, and what you'll demonstrate.
Ship a drift-triggered retraining pipeline that auto-evaluates and promotes new models, with a compliance-friendly manual gate.
Earning criteria — what you'll demonstrate
- Implement an automated retraining pipeline with MLflow + Airflow
- Set drift-detection thresholds that fire on real shifts, not noise
- Design a promote-on-win gate with compliance-friendly approvals
- Document an audit-ready retraining process
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
ML Engineering and Production ML
Master · Ai Systems
Strong alignment
This challenge maps to ML Engineering and Production ML 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.
- Mlflow
Apply mlflow to solve real industry problems and demonstrate production-level capability.
- Airflow
Apply airflow to solve real industry problems and demonstrate production-level capability.
- Data Drift Detection
Apply data drift detection to solve real industry problems and demonstrate production-level capability.
- Model Registry
Apply model registry to solve real industry problems and demonstrate production-level capability.
- Automated Retraining
Apply automated retraining to solve real industry problems and demonstrate production-level capability.
- Compliance
Apply compliance 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
Drift-triggered retraining pipelines with MLflow + Airflow are the platform-MLOps work that regulated AI teams need to scale beyond manual retraining.
This challenge sharpens
- mlflow
- airflow
- automated-retraining
Machine Learning Engineer
MLEs increasingly own the retraining lifecycle end to end; this challenge gives a strong portfolio piece for that capability.
This challenge sharpens
- automated-retraining
- data-drift-detection
- model-registry
Data Engineer
Building the Airflow DAGs and data flows that underpin automated retraining is the data-engineering side of any ML platform team.
This challenge sharpens
- airflow
- data-drift-detection
- model-registry