This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on Security & Compliance for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand basic security risks in MLOps and learn how to protect your data, models, and systems during the ML lifecycle.
✓ Set up access control, secrets management, and secure environments for running machine learning pipelines.
✓ Learn how to follow key compliance rules like GDPR, HIPAA, and ISO 27001 when working with ML models and sensitive data.
✓ Use tools like MLflow, Kubernetes, and Vault to track, protect, and explain ML models for audits and safe production use.
There are 10 modules in this course
- Introduction
- Security in the ML Lifecycle
- Data Security
- Model Security
- Infrastructure & Pipeline Security
- Compliance in MLOps
- Responsible AI & Ethics
- Tooling & Frameworks
- Case Studies
- Capstone Project
About this course
In this course, you will learn why security and compliance are important parts of MLOps and how they help keep Machine Learning systems safe and trustworthy. When ML models move into production, they often deal with sensitive data, real users, and business-critical tasks. Without proper security, these systems can face serious risks. Without compliance, companies may break rules or lose user trust.
We will start with simple ideas and explain how security fits into the ML lifecycle – from storing and handling data to running models and managing pipelines. You will also learn what compliance means in real projects, how teams stay within rules, and what documents or checks are often required. This course is not about building ML models, but about keeping them safe, fair, and legal after they are deployed.
You will understand the kind of problems that can happen, like data leaks, model misuse, or unfair predictions, and how MLOps teams can prevent them. We will also look at daily tasks of MLOps Engineers who focus on security and how they work with others to keep systems in good shape.
By the end of this course, you will see how security and compliance work together with other parts of MLOps. You will know what questions to ask, what to look out for, and how to build ML systems that are not only fast and scalable — but also responsible and trusted.
Start learning high demand tech skills today
Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.
In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.
You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.
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