This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on CI/CD for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand how CI/CD works for machine learning systems, and why it is important for real-world ML projects.
✓ Build simple but powerful pipelines using GitHub Actions, GitLab CI, or ArgoCD to test, build, and deploy ML models.
✓ Learn how to track model versions and deploy updates safely using tools like MLflow and Docker.
✓ Set up feedback loops and triggers to monitor models in production and improve them over time.
There are 10 modules in this course
- Introduction
- Continious Integration (CI)
- Automated Model Testing & Validation
- Continuous Delivery & Continuous Deployment
- GitOps and ML Deployment Tools
- Model Registry and Integration in Pipelines
- Monitoring, Alerting & Feedback Loops
- CI/CD Patterns and Tools
- Common Pitfalls and Best Practices
- Capstone Project
About this course
In this course, you will learn how CI/CD (Continuous Integration and Continuous Delivery) helps Machine Learning projects work better in real environments. Many ML models are trained once and never used again, or they break when moved to production. CI/CD helps teams test, build, and update ML systems in a repeatable and reliable way.
We will start by understanding the idea of CI/CD in the context of MLOps. You will see how ML pipelines are different from regular software pipelines, and what steps are needed to keep models, code, and systems working well together. You will also learn how to check model quality, deploy updates, and monitor changes over time – all as part of a structured workflow.
This course is not about building new models. It’s about making sure the models your team already has can be tested, improved, and safely delivered into production again and again. You will explore the key ideas behind versioning, approval flows, testing logic, and automation – everything that helps MLOps Engineers bring stability and speed to ML operations.
By the end of the course, you will understand how to design and manage ML pipelines that are easier to maintain and scale. This knowledge is essential for anyone working in MLOps, especially if you want to support ML in real-world use, where things change fast and must be managed carefully.
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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