The course is part of this learning path
This course from Kevin McGragh, VP of Architecture at Spotinst.com explains how to leverage excess cloud capacity from providers such as AWS, Microsoft Azure, and Google Cloud Platform to optimize the costs of cloud computing using spot instances.
Everyone working with Cloud compute workloads, from start-ups to large corporations.
A basic understanding of cloud computing and cloud computing billing models. if you are new to cloud computing we recommend completing our What is Cloud Computing course first.
This course will enable you to:
- Recognize and explain how to run and manage workloads on excess cloud capacity using Spot Instances.
- Recognize and explain the risks and benefits of the spot market.
- Recognize and implement Spot Instances to reduce cloud compute costs.
- AWS spot instances - How do they work?
- Overview, Basic Concepts and First Steps
- The Advantages of Spot Instances
- Best Practices, Workloads and Use Cases
If you have thoughts or suggestions for this course, please contact Cloud Academy at firstname.lastname@example.org.
About the Author
Kevin McGrath is the VP of Architecture for Spotinst, specializing in Cloud Native and Serverless product designs. Kevin is responsible for researching and evaluating innovative technologies and processes leaning on his extensive background in DevOps and delivering Software as a Service within the communications and IT infrastructure industry.
Kevin started his career at USi, the first Application Service Provider (ASP) 20 years ago. It was here he began delivering enterprise applications as a service in one of the first multi-tenant shared datacenter environments. After USinternetworking was acquired by AT&T, Kevin served in the office of the CTO at Sungard Availability Services where he specialized in migrating legacy workload to cloud native and Serverless architectures.
Kevin holds a B.A. in Economics from the University of Maryland and a Masters in Technology Management from University of Maryland University College.