DevOps and AI on AWS: AIOps
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DevOps and AI on AWS: AIOps
This course is part of DevOps and AI on AWS Specialization
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What you'll learn
Understand AIOps and its role in addressing operational challenges.
Implement AI-driven monitoring to reduce alarm fatigue and information overload.
Skills you'll gain
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4 assignments
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There are 2 modules in this course
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 itβs time to do the same for our DevOps processes. With our travel guide now in production, letβs dive into the challenges weβll face as we scale β and how we can mitigate those challenges. As we scale, weβll 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 whatβs actionable, or, in some cases, you donβt have enough of the right information and visibility to make informed decisions. Thatβs 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. Letβs learn how AIOps can help streamline operations, improve the way we monitor applications, and automate responses to common problems.
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.
What's included
7 videos6 readings2 assignments1 app item1 plugin
7 videosβ’Total 32 minutes
- Introduction to the Courseβ’6 minutes
- Module 1 Introductionβ’1 minute
- Observabilityβ’5 minutes
- AIOps Fundamentalsβ’5 minutes
- Amazon CloudWatch Anomaly Detectionβ’6 minutes
- AWS X-Ray Insightsβ’3 minutes
- Amazon DevOps Guruβ’6 minutes
6 readingsβ’Total 41 minutes
- Welcome to the Courseβ’5 minutes
- Pre-Course Surveyβ’1 minute
- Course Roadmapβ’5 minutes
- Observabilityβ’5 minutes
- AI Ops Fundamentalsβ’10 minutes
- Amazon Devops Guruβ’15 minutes
2 assignmentsβ’Total 50 minutes
- Module 1 Quizβ’30 minutes
- Pre-Assessmentβ’20 minutes
1 app itemβ’Total 60 minutes
- Instrument the Application and View Traces on X-Rayβ’60 minutes
1 pluginβ’Total 10 minutes
- Pre-Course Surveyβ’10 minutes
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.
What's included
3 videos3 readings2 assignments1 plugin
3 videosβ’Total 10 minutes
- Module 2 Introductionβ’1 minute
- Amazon Q Developer Security Scanningβ’5 minutes
- Course Closingβ’4 minutes
3 readingsβ’Total 31 minutes
- Amazon Q Developer Security Scanningβ’15 minutes
- Post-Course Surveyβ’1 minute
- Glossaryβ’15 minutes
2 assignmentsβ’Total 90 minutes
- Self-Reflectionβ’30 minutes
- Post Assessmentβ’60 minutes
1 pluginβ’Total 10 minutes
- Post-Course Surveyβ’10 minutes
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Reviewed on May 13, 2025
I can understand more about AWS services that they can apply in GenAI projects. This course can help me to work in my project.
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you canβt afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, youβll find a link to apply on the description page.
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