1. Clone the repository
2. Set up AWS and Snowflake credentials (see config/ for templates)
3. Prepare data in Snowflake using provided SQL script
4. Train models with provided Jupyter notebooks/scripts
5. (Optional) Deploy model to AWS SageMaker endpoint

Results

Metric	Value
Accuracy	92.1%
AUC-ROC	0.89
Data Volume	2 million rows processed
Training Time	~12 minutes on ml.m5.large

All metrics are from the provided sample dataset (replace with real values if available).

An end-to-end, production-ready pipeline that ingests raw transaction data into Snowflake, trains an XGBoost fraud-detection model on SageMaker, and serves real-time predictions via a Flask API.

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Table of Contents

1. Overview
2. Architecture
3. Installation & Quick Start
4. Usage
5. Quantifiable Metrics
6. Project Structure
7. Running Tests
8. Contributing
9. License

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Overview

A modular, production-style machine learning pipeline integrating AWS SageMaker for scalable model training and Snowflake as a cloud data warehouse, designed for real-world enterprise data science workflows. Financial fraud detection requires both accurate models and reliable, scalable infrastructure. This project demonstrates:

• Data Ingestion: Copy raw transaction CSVs into a Snowflake staging table.
• Feature Engineering & Processing: Run a SageMaker Processing job that cleans and transforms data.
• Model Training: Train an XGBoost classifier on SageMaker (ml.m5.2xlarge), achieving high AUC.
• Real-time Inference: Host the trained model on a SageMaker endpoint and expose a Flask API on EC2 for predictions.
• Dashboard Integration (Optional): Serve inference results to a downstream dashboard or service.

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Architecture

1. Raw Data (S3) → Snowflake
   • Snowflake X-Small warehouse loads and stores raw transactions.
2. SageMaker Processing
   • Pulls from Snowflake, applies feature engineering, and writes processed data back.
3. SageMaker Training
   • Reads processed data, trains an XGBoost model, and saves the artifact to S3.
4. SageMaker Endpoint
   • Deploys the model for real-time inference (ml.t2.medium).
5. Flask API (EC2 t3.medium)
   • Forwards JSON transaction payloads to the SageMaker endpoint and returns fraud probability.
6. Dashboard / Client
   • Consumes API responses to visualize fraud scores.

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Installation & Quick Start

Prerequisites

• Docker (≥ 19.x)
• AWS credentials with permissions for SageMaker, S3, and IAM
• Snowflake account + SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, and role with privileges

Clone & Build

git clone https://github.com/Trojan3877/AWS-SageMaker-Snowflake-ML-Pipeline.git
cd AWS-SageMaker-Snowflake-ML-Pipeline