In today’s fast-paced digital world, businesses are seeking to transform data into insights more quickly than ever. But building an AI/ML pipeline isn’t just about training a model—it’s about creating a robust, scalable workflow that ensures reproducibility, monitoring, and business value.
In this case study, we walk you through the development of an end-to-end AI/ML pipeline to predict customer churn for a subscription-based e-commerce company.
🎯 Project Goal
The business wanted to predict whether a customer would cancel their subscription in the next 30 days. This would help the marketing team intervene with retention strategies and reduce churn.
🏗️ Step 1: Data Collection and Ingestion
We started by identifying key data sources:
- Customer transactions (PostgreSQL)
- Web clickstream data (S3 in JSON)
- CRM system logs (via REST API)
To automate ingestion, we built a data pipeline using Apache Airflow. Data was extracted daily, cleaned, and stored in AWS S3 in partitioned Parquet format for efficient downstream processing.
Tools Used:
- Airflow
- Python (pandas, requests)
- AWS S3
- PostgreSQL
🧹 Step 2: Data Preprocessing and Feature Engineering
With data in S3, we moved to AWS Glue for data wrangling. Key tasks:
- Handle missing values (e.g., fill with median)
- Create rolling aggregates like average order value over 90 days
- Encode categorical variables (one-hot and label encoding)
We stored processed features in an Amazon Redshift data warehouse for quick access.
Notable Techniques:
- Time-based feature engineering
- Categorical encoding
- Outlier removal
🤖 Step 3: Model Training
We pulled the clean data into a Jupyter notebook using Amazon SageMaker Studio and trained multiple models using Scikit-learn:
- Logistic Regression
- Random Forest
- XGBoost
After hyperparameter tuning using GridSearchCV, the XGBoost model gave the best performance:
- Accuracy: 89%
- ROC AUC: 0.94
Model Versioning: We tracked models using MLflow, saving artifacts and metrics.
🚀 Step 4: Model Deployment
The trained model was packaged using Flask and deployed on an AWS EC2 instance behind a load balancer.
Deployment involved:
- Dockerizing the inference API
- Setting up auto-scaling based on CPU load
- Logging inference results to CloudWatch
This allowed any internal system (e.g., CRM) to hit the API and get churn predictions in real time.
Security: IAM roles and API Gateway with token-based authentication.
📊 Step 5: Monitoring and Retraining
Monitoring was key:
- Input data drift detection using EvidentlyAI
- Model accuracy tracking with real labels after a delay
- Automated retraining pipeline triggered every 2 weeks via Airflow
Dashboards were built using Grafana and Prometheus to monitor:
- API latency
- Prediction volume
- Accuracy trends over time
🧠 Key Takeaways
- MLOps is not optional – version control, automation, and monitoring are critical.
- Building for scalability from day one avoids rework.
- Cross-functional collaboration (Data Engineers, ML Engineers, DevOps) is key.
📈 Business Impact
After deployment, the marketing team used the predictions to launch targeted campaigns. The result?
- 22% reduction in monthly churn
- 3.5x ROI within the first quarter
- Executive sponsorship for future ML initiatives
🛠️ Tools Used in the Pipeline
| Stage | Tools & Technologies |
|---|---|
| Ingestion | Airflow, Python, PostgreSQL, S3 |
| Processing | AWS Glue, Pandas, Redshift |
| Training | SageMaker, Scikit-learn, XGBoost |
| Deployment | Flask, Docker, EC2, API Gateway |
| Monitoring | MLflow, EvidentlyAI, Grafana |
📌 Final Thoughts
Building an AI/ML pipeline is much more than model building. It involves understanding business needs, data engineering, automation, deployment strategies, and long-term maintainability.
This case study highlights how a small, focused team built a production-grade pipeline that directly impacted business outcomes. Whether you’re in retail, finance, or healthcare, these principles apply universally.
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