Ad-Click Prediction MLOps
Enterprise-grade pipeline for predicting real-time ad clicks.
The Problem
Digital marketing teams often face waste in ad spend due to poor targeting. Predicting user click behavior in real-time is essential for optimizing bidding strategies and ensuring ads reach the most relevant users.
Key Decisions
- ZenML & MLflow: I chose ZenML for pipeline orchestration and MLflow for experiment tracking to ensure a fully reproducible and versioned model lifecycle.
- Evidently AI: Integrated for data drift detection to trigger automated retraining, ensuring the model remains accurate as user behavior evolves.
- FastAPI: Selected for model serving to achieve the sub-50ms latency required for real-time bidding environments.
Results
| Metric | Value |
|---|---|
| Accuracy | 83.4% |
| ROC-AUC | 0.88 |
| Serving Latency | < 50ms |
What I Learned
Building this project emphasized that “Model is only 5% of the work”—the real challenge is the orchestration, monitoring, and automated retraining loops that keep the system alive in production.
[Built as an MLOps showcase. Source code is public.]