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Amazon Web Services

DevOps and AI on AWS: AIOps

Amazon Web Services via Coursera

Overview

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In this course, we focus on how we can use AI techniques to improve our DevOps operational efficiency. We have added AI features to our applications, now it’s time to do the same for our DevOps processes. With our travel guide now in production, let’s dive into the challenges we’ll face as we scale – and how we can mitigate those challenges. As we scale, we’ll undoubtedly experience some monitoring alarms as we scan our development environment. In this scenario, information overload without the right tools can leave you stuck: you either have too much data with no clear direction on what’s actionable, or, in some cases, you don’t have enough of the right information and visibility to make informed decisions. That’s where AIOps can make a huge difference. AIOps is the process of using machine learning techniques to solve operational problems. The goal of AIOps is to reduce human intervention in the IT operations processes, reduce operational incidents, and improve your applications. Let’s learn how AIOps can help streamline operations, improve the way we monitor applications, and automate responses to common problems.

Syllabus

  • Module 1: AIOps on AWS
    • This module introduces the concept of AIOps (Artificial Intelligence for IT Operations) and how it can enhance observability and address specific challenges in DevOps processes. It covers various AWS services and tools that leverage AI/ML to detect anomalies, identify performance issues, and improve operational efficiency.
  • Module 2: Improving Developer Experience with AI
    • This module focuses on leveraging AI/ML to enhance the developer experience and improve code quality and security. It covers AWS services like Amazon CodeGuru and Amazon CodeCatalyst, which use AI/ML to provide intelligent code reviews, identify vulnerabilities, and offer personalized recommendations.

Taught by

Russell Sayers and Rafael Lopes

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