FeaturedUdemy
Fully Automated MLOps
4.8(337)6k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate MLOps course focuses on achieving end-to-end automation in machine learning deployment pipelines. It details key Architectural components required to reduce technical debt, emphasizing Continuous Integration (CI), Continuous Delivery (CD), Continuous Monitoring (CM), and Continuous Training (CT) to maintain production model reliability.
Who It's For
Machine learning engineers, DevOps practitioners, and data scientists looking to automate model deployment pipelines and build scalable production ML architectures.
Key Takeaways
- ML Technical Debt & Architecture: Identify technical debt risks in machine learning systems and design robust MLOps architectures.
- CI/CD/CM/CT Pipelines: Implement Continuous Integration, Continuous Delivery, Continuous Monitoring, and Continuous Training for automated lifecycle management.
- Automated Deployment Strategies: Deploy machine learning models with automated fallback, health checks, and continuous model updating.
About this course
MLOps is the set of practices developed to help you deploy and maintain machine learning models in production. Nowadays, in industry and research, MLOps is in the spotlight as a way to ensure that ML systems produce value.
Discover Full Automation in MLOps
In this course, you will learn how to use automation in MLOps to deploy ML systems that can deliver value over time. You'll learn how hidden technical debt affects ML systems and the value they produce. You'll also understand how automating and streamlining the stages of the ML lifecycle can help the operation and scaling of ML systems.Learn About MLOps Architecture
You will use hands-on and interactive exercises to learn about the components of an MLOps architecture and how these are necessary to enable the full automation of ML systems.Explore CI/CD/CM/CT MLOps Techniques
As you progress, you’ll learn how automated CI/CD, together with Continuous Monitoring (CM) and Continuous Training (CT), are key techniques to avoid technical debt in your ML deployments.Understand Automation in Deployment Strategies
By the end of the course, you’ll understand how automation with MLOps can improve how you deploy your ML systems to the real world, providing your deployments with robustness and scalability.Start learning, gain knowledge in this highly in-demand field, and discover how to apply automation when designing MLOps systems.
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