06Azure ML for Retail Churn Prediction (AutoML vs Code)
Role: Solution Engineer / Data Scientist
Executive summary
Ran a head-to-head AutoML vs custom-PyTorch churn study on Azure ML with MLflow tracking and SHAP explainability; AutoML won (AUC 0.88 vs 0.84) and was deployed as a scoring endpoint.
- Azure Machine Learning
- AutoML
- PyTorch
- MLflow
- SHAP
- Online Endpoints
A retail client wanted to predict customer churn on Azure ML and to compare AutoML against a custom-code approach to decide what fit their team. The project also required MLflow tracking and an exploration of PyTorch.
- Set up an Azure ML AutoML experiment to train/tune multiple churn classifiers on historical data.
- Develop a custom PyTorch churn model and train it on Azure ML for comparison.
- Integrate both with MLflow for fair, version-controlled comparison.
- Produce a report comparing accuracy, explainability and development effort.
- Deploy the chosen model to an Azure ML online endpoint.
I preprocessed customer data (demographics, purchases, support calls) in Azure ML, configured an AutoML run (label = churn, metric = AUC, algorithm sweep), and wrote a PyTorch training script for a feed-forward network on a compute cluster. Both logged to Azure ML tracking. I used SHAP for the top AutoML model and compared feature importance, reviewed results with marketing analysts, then deployed the best model to a test endpoint for a live demo.
AutoML outperformed the initial PyTorch model (AUC ~0.88 vs ~0.84) with far less effort, and its tree-ensemble gave better interpretability (recent purchase frequency was the top churn driver). The client got a clear view of trade-offs and chose AutoML for production ease, while retaining custom modeling for future enhancements. The endpoint let their CRM flag likely churners for retention campaigns.
AutoML is highly effective for common scenarios like churn and often beats a first-pass custom model—provided you interpret results to build trust. Helping clients evaluate options builds confidence in the solution. PyTorch integrates smoothly with Azure ML, but its overhead is not always warranted.