DevOps Platforms and Operations: AIOps & MLOps Basics

DevOps    |    Beginner
  • 14 Videos | 1h 25m 8s
  • Includes Assessment
  • Earns a Badge
MLOps is used to scale and govern machine learning operations, while AIOps provides a way to solve business problems and reduce incident management overhead using AI. In this course, you'll begin with an introduction to what AIOps is and which common IT operations it can be applied to. Discover AIOps capabilities, main business areas which AIOps can be incorporated into, and the benefits to an organization considering adoption. Next, explore various business use cases suitable for AIOps and adoption from a business and technical perspective. Moving on, look at the capabilities provided by MLOps and the benefits of MLOps to a data scientist. Next, explore the steps involved in implementing MLOps including the tools, challenges, and best practices involved. Upon completion of this course, you'll be able to identify the differences between AIOps, MLOps, and DevOps.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize the uses for artificial intelligence (AI) in IT operations
    recognize opportunities to implement AIOps within an organization
    outline an approach for implementing AIOps
    list examples of common AIOps tools and how to evaluate them for your requirements
    outline best practices for an AIOps implementation
    recognize when and why to adopt a machine learning operations (MLOps) practice
  • recognize the uses for MLOps from the perspective of a data scientist
    outline the steps required to implement MLOps in an organization
    identify MLOps tools and how to evaluate them for your requirements
    recognize challenges you might face when implementing an MLOps practice
    outline best practices for an MLOps implementation
    identify the differences between AIOps, MLOps, and DevOps and when to use each
    summarize the key concepts covered in this course

IN THIS COURSE

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