learning path

Measuring Performance - Level 2

Intermediate
34m
11
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A Service Level Agreement (SLA) is a contract between an IT service provider and its customers that defines the level of service that the provider will deliver. SLAs are an important tool for ensuring that a system or service meets the desired levels of performance and availability. In this learning objective, we will explore the concept of SLAs in more detail, and discuss how they are used to manage system uptime.

We will begin by looking at the different components of an SLA, including the service level objectives (SLOs) and service level indicators (SLIs) that are used to define the desired levels of performance and availability. We will then discuss how SLAs are used to monitor and enforce these objectives, and how they can be used to provide customers with visibility into the performance and availability of the service.

Finally, we will look at how to define and establish appropriate SLAs for your own systems and services. This will involve setting realistic and achievable SLOs and SLIs, and establishing processes for monitoring and enforcing the SLA. By the end of this learning objective, you will have a solid understanding of the role of SLAs in managing system uptime, and you will be able to define and establish appropriate SLAs for your own systems and services.

This learning path teaches Measuring Performance to a level 2 standard. 

Learning Objectives

  • Working with SLAs (Service Level Agreements) and and how they relate to system uptime
  • Be able to define and establish appropriate SLAs
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About the Author
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Jeremy Cook
Content Lead Architect
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Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.

Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).

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