Secure AI Pipelines MLOps DevSecOps Essentials

Description

Learn how to build secure AI pipelines using MLOps and DevSecOps best practices. In this video, you’ll understand how modern companies protect machine learning systems across the entire lifecycle, from data collection to deployment and continuous monitoring. We break down the key differences between MLOps, DevOps, DataOps, and ModelOps, and show how security must be embedded directly into AI pipelines, not added at the end. 🚀 What you’ll learn: What MLOps really is (and why it’s more than DevOps for ML) How DevSecOps integrates security into CI/CD pipelines The complete MLOps lifecycle: data, training, deployment, monitoring How to detect model drift and automate retraining Security controls for AI: encryption, RBAC, artifact signing Real-world scenarios: fraud detection, healthcare AI, IoT models Best practices for scalable and secure AI systems 🔐 Why this matters: AI systems introduce new risks, including data poisoning, model drift, and insecure deployments. Without proper controls, your ML pipeline becomes a major attack surface. This video shows how to reduce risk while scaling AI across your organization. 💡 Perfect for: Cybersecurity professionals ML engineers and data scientists DevOps / DevSecOps engineers Anyone building or deploying AI systems in production 📌 Key takeaway: Secure AI is not optional. It must be designed into your pipelines from day one.