This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on Cloud for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand how to set up and manage machine learning systems in cloud or hybrid environments.
✓ Learn how to monitor model health, track changes, and handle failures in Cloud.
✓ Use tools like MLflow, Airflow, and Kubernetes to automate and control ML pipelines.
✓ Explore real-world ways to manage model versions, scaling, and resource usage in Cloud.
There are 11 modules in this course
- Introduction
- Key Cloud Services for MLOps
- Building Cloud-Native ML Workflows
- Deployment Strategies in Cloud
- Identity, Access, and Resource Management (IAM)
- Cost Optimization and Resource Efficiency
- Cloud-Native Model Management
- Monitoring and Observability of ML in Cloud
- Compliance, Data Residency, and Governance
- Case Studies
- Capstone Project
About this course
In this course, you will learn how cloud technologies support the work of MLOps engineers. When machine learning moves from experiments to real systems, the cloud becomes a key part of how we build, deploy, and manage those systems at scale.
We will look at how MLOps teams use cloud platforms to run training jobs, store data and models, manage resources, and handle day-to-day operations. You will see how cloud-based environments help with automation, scaling, versioning, and cost control – all important parts of a stable and reliable ML system.
This course focuses on real tasks that MLOps engineers do in cloud environments – choosing the right compute and storage options, designing workflows for training and serving models, managing identity and access, and keeping systems secure and cost-efficient.
By the end of the course, you will understand how to think about cloud design choices in MLOps, how to make workflows repeatable and safe, and how to avoid common mistakes. You will also learn how to work across teams and keep systems running well in different stages of the ML lifecycle.
The goal of this course is to help you build a strong foundation in cloud-based MLOps. It is part of a full learning path, and after it, you can explore deeper topics like infrastructure, observability, security, or cost optimization.
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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