Secure-AI-MLOps-Pipeline

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Secure-AI-MLOps-Pipeline

​I’m excited to share my latest project: GuardAI Pipeline — an end-to-end Secure MLOps Framework. ​As a student passionate about Data & AI, I didn't just want to build a model; I wanted to build a Production-Ready system that respects data privacy and security (ISC² standards). ​Key Highlights: ✅ Data Anonymization: SHA-256 hashing for PII protection. ✅ Sanitization Layer: Protecting pipelines from injection attacks. ✅ Quality Audit: Automated data integrity checks. ✅ MLOps Integration: Full experiment tracking with MLflow. ​This pipeline is environment-agnostic, running on Microsoft Fabric and Google Colab alike. ​📂 Check out the full source code on GitHub:

Built With

• mlflow
• pyspark
• scikit-learn