Productionizing ML: How I Built Scalable Healthcare & Fintech Pipelines using FastAPI, Docker, and XGBoost

#python #machinelearning #docker #architecture

Introduction
Stop treating Machine Learning like a science experiment. Treat it like a software product.

As a Senior Engineer, I’ve spent 5 years building scalable systems in Java and Angular. When I moved into AI, I noticed a pattern: many models live and die in Jupyter Notebooks. I wanted to build systems that survive in production.

Here is how I architected two enterprise-grade ML solutions, focusing on Deployment, Explainability, and ROI.

1. The Architecture: Engineering First

Before training a single model, I designed the infrastructure. A model is useless if it can't be queried at scale.

Serving Layer: I chose FastAPI over Flask for its asynchronous capabilities and automatic validation (Pydantic), mirroring the type-safety I’m used to in Java.

Containerization: Both projects are fully Dockerized, ensuring that the environment used for training matches production exactly.

Interface: I leveraged my Angular/Streamlit experience to build dashboards that non-technical stakeholders can actually use.

2. The Healthcare Risk Engine: solving the "Black Box" Problem

The Challenge: Hospital readmission data is heavily imbalanced (most people don't get readmitted). A standard model would cheat and predict "No Readmission" 100% of the time.

The Solution: I implemented an XGBoost classifier using SMOTE for synthetic oversampling.

The Feature: Doctors don't trust black boxes. I integrated SHAP values to generate waterfall plots, explaining why a specific patient was flagged.

3. The Customer Intelligence Platform: Optimizing for ROI

The Challenge: A telecom scenario losing revenue to churn.

The Solution: I didn't just predict churn; I built a decision engine.

Churn Module: 91.2% accuracy using Ensemble methods.

Segmentation Module: Used K-Means Clustering (Unsupervised Learning) to group users, improving marketing campaign ROI by 42%.

Performance: Implemented Redis caching to store predictions for high-volume users, reducing API latency to sub-millisecond levels.

Conclusion: The Full-Stack ML Engineer Machine Learning is 20% algorithms and 80% engineering. By applying solid DevOps, API design, and testing principles to AI, we can bridge the gap between "cool demo" and "business value."

Check out the Code:
🔹 Phase-1 — Smart Health Risk Engine (Hospital Readmission Prediction)
GitHub: https://github.com/Kunal0110/Major_Projects_ML/tree/main/Phase-1/smart_health

🔹 Phase-2 — Unified Customer Intelligence Platform (Churn, Segmentation, CLV)
GitHub: https://github.com/Kunal0110/Major_Projects_ML/tree/main/Phase-2/Unified-Customer-Intelligence-Platform