🚀 Building a SageMaker Pipeline to Train & Deploy a RoBERTa Fake News Detection Model

A fully automated AWS SageMaker Pipeline that ingests a raw “fake news” dataset, cleans & balances it, trains a RoBERTa classifier, evaluates its performance, and—if it meets your quality gates—packages & registers the model for deployment after human approval.

🏗️ Architecture

1. Data Registration & understanding
2. Pipeline Definition
3. Processing (clean, balance, transform, split)
4. Training (train on train+validation)
5. Evaluation (test the trained model's performance on the test dataset)
6. Conditional Model Registration
7. Human approval and SageMaker endpoint deployment

---

⚙️ Prerequisites

• Python 3.8 or above
• AWS account with permissions for SageMaker, S3, IAM, CloudWatch
• AWS CLI v2 configured
• boto3, sagemaker, transformers

pip install boto3 sagemaker protobuf transformers pandas