AutoMLOps-Cloud
AutoMLOps-Cloud demonstrates a complete, end-to-end MLOps pipeline for predicting customer purchasing behavior, built on Amazon SageMaker. The entire workflow—from data processing and feature engineering to model training and deployment—is fully automated and cloud-native, allowing for reproducible, scalable, and maintainable ML operations.
Key Features:
- End-to-end automation from data ingestion to prediction storage
- Modular, containerized design supporting local/SageMaker runs
- Fully cloud-native, leveraging S3, Lambda, and Step Functions
- Unified codebase for training, batch inference, and API serving
- Real-world use for business purchase prediction
System Architecture
This repository’s core is a fully containerized workflow, allowing for identical model training and inference processes on both local machines and AWS SageMaker.
Dockerized architecture: Unified logic for both local and cloud workflows, ensuring portability and reproducibility.
Real-World Value
This architecture serves as a practical blueprint for deploying real-time customer prediction systems in business settings, drastically reducing deployment cycles and making rapid model iteration easy.