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Morgan Willis, Rafael Lopes, and Russell Sayers

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.

What's inside

Learning objectives

  • Understand aiops and its role in addressing operational challenges.
  • Implement ai-driven monitoring to reduce alarm fatigue and information overload.
  • Apply machine learning techniques to derive actionable insights from operational data.
  • Integrate aiops practices with existing devops workflows on aws.
  • Utilize aws aiops services to predict and prevent application issues.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on using AI to improve DevOps operational efficiency, which is a growing trend in the tech industry
Teaches how to reduce human intervention in IT operations, which can lead to increased efficiency and reduced costs
Utilizes AWS AIOps services to predict and prevent application issues, which is valuable for those invested in the AWS ecosystem
Integrates AIOps practices with existing DevOps workflows, which helps learners implement these techniques in their current environments
Assumes familiarity with DevOps processes, so learners new to DevOps may need to acquire foundational knowledge first

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Reviews summary

Practical aiops concepts on aws

According to learners, this course provides a solid and practical introduction to AIOps concepts specifically on AWS. Students particularly highlight the hands-on labs using AWS services like CloudWatch and Lookout for Metrics as being extremely helpful and relevant for understanding how AIOps integrates into existing DevOps workflows. Many found the explanations of complex AIOps ideas to be clear and simple. While the course is seen as a good starting point, some reviewers felt certain ML concepts were simplified and that deeper understanding might require additional resources. A few mention encountering minor issues or needing troubleshooting tips for the labs, suggesting there might be occasional outdated instructions or permission challenges.
Explains AIOps simply.
"Excellent course explaining AIOps concepts on AWS."
"I finally understand how AIOps fits into my workflow."
"The instructors did a great job explaining complex ideas simply."
"The AIOps framework presented is logical."
Hands-on labs highly praised.
"Labs were very helpful and practical."
"The integration points with existing AWS services like CloudWatch and Lookout for Metrics were clearly demonstrated."
"The hands-on labs using AWS services were the best part."
"Great labs."
"seeing it applied on AWS services makes it tangible. Great labs."
Some labs have issues.
"Some labs had issues with permissions or outdated instructions."
"Wish there were more troubleshooting tips for the labs, as I got stuck a few times."
Foundation but needs more.
"It provides a good starting point, but you'll need more resources for deep understanding."
"The ML concepts felt a bit simplified, but the focus was on practical application on AWS."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in DevOps and AI on AWS: AIOps with these activities:
Review AWS CloudWatch Metrics
Reviewing AWS CloudWatch metrics will help you better understand the data sources used in AIOps for monitoring and anomaly detection.
Show steps
  • Explore the CloudWatch console in the AWS Management Console.
  • Review key metrics for EC2 instances, Lambda functions, and other AWS services.
  • Practice creating custom metrics and dashboards.
Read 'Effective DevOps'
Reading this book will provide a strong foundation in DevOps principles, which are essential for understanding AIOps.
View Effective DevOps on Amazon
Show steps
  • Obtain a copy of 'Effective DevOps'.
  • Read the chapters focusing on DevOps principles and practices.
  • Take notes on key concepts and examples.
Read 'Practical AIOps: Automating IT Operations with AI'
Reading this book will provide a broader understanding of AIOps concepts and real-world applications, complementing the course material.
View Melania on Amazon
Show steps
  • Obtain a copy of 'Practical AIOps: Automating IT Operations with AI'.
  • Read the chapters focusing on machine learning applications in IT operations.
  • Take notes on key concepts and examples.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice with AWS AIOps Services
Practicing with AWS AIOps services will help you gain hands-on experience with the tools and techniques covered in the course.
Show steps
  • Explore AWS services like Amazon DevOps Guru and Amazon Lookout for Metrics.
  • Set up monitoring and anomaly detection using these services.
  • Experiment with different configurations and settings.
Blog Post: AIOps Use Cases on AWS
Creating a blog post about AIOps use cases on AWS will solidify your understanding of the course material and allow you to share your knowledge with others.
Show steps
  • Research different AIOps use cases relevant to AWS environments.
  • Choose a specific use case to focus on (e.g., automated incident resolution).
  • Write a blog post explaining the use case, its benefits, and how to implement it on AWS.
  • Publish the blog post on a platform like Medium or your personal website.
Implement Anomaly Detection on Sample Data
Implementing anomaly detection on sample data will provide hands-on experience with applying machine learning techniques to operational data.
Show steps
  • Gather sample operational data (e.g., CPU utilization, network traffic).
  • Choose a suitable anomaly detection algorithm (e.g., Isolation Forest, One-Class SVM).
  • Implement the algorithm using a machine learning library (e.g., scikit-learn).
  • Evaluate the performance of the anomaly detection model.
Create a Presentation on AIOps Benefits
Creating a presentation on the benefits of AIOps will help you synthesize your knowledge and communicate it effectively.
Show steps
  • Research the benefits of AIOps in different contexts.
  • Create a presentation outlining these benefits, including specific examples and case studies.
  • Practice delivering the presentation to a friend or colleague.

Career center

Learners who complete DevOps and AI on AWS: AIOps will develop knowledge and skills that may be useful to these careers:
DevOps Engineer
A DevOps Engineer bridges the gap between software development and IT operations. In this role, you'll automate and streamline processes to enable faster and more reliable software releases. This course fits perfectly, as it delves into using AI to improve DevOps operational efficiency, directly addressing the challenges faced when scaling applications and managing monitoring alarms. Learning how to implement AI driven monitoring to reduce alarm fatigue and information overload allows a DevOps Engineer to streamline operations, improve application monitoring, and automate responses to common problems. Focusing on AIOps practices helps integrate these with existing DevOps workflows.
Site Reliability Engineer
Site Reliability Engineers focus on ensuring the reliability, availability, and performance of systems. This course is highly relevant, as it teaches how to use AI to improve operational efficiency, reducing human intervention in IT operations. Learning how to implement AI driven monitoring to reduce alarm fatigue and information overload allows a Site Reliability Engineer to streamline operations, improve application monitoring, and automate responses to common problems. The course emphasizes integrating AIOps practices with existing DevOps workflows, which is essential for maintaining high system reliability, especially with the goal to reduce operational incidents and improve applications.
IT Automation Engineer
An IT Automation Engineer automates repetitive tasks and processes within IT operations, improving efficiency and reducing errors. This course is exceedingly helpful. You'll learn how AIOps streamlines operations and automates responses to common problems. This directly translates into more efficient and effective automation strategies. Learning how to implement AI driven monitoring to reduce alarm fatigue and information overload allows an IT Automation Engineer to improve application monitoring, and automate responses to common problems. The goal of AIOps is to reduce human intervention in the IT operations processes.
Cloud Engineer
Cloud Engineers implement, manage, and maintain cloud computing systems and services. This course is beneficial, as it focuses on how AI can improve DevOps operational efficiency, a key aspect of managing applications in the cloud. Implementing AI driven monitoring to reduce alarm fatigue and information overload allows a Cloud Engineer to streamline operations, improve application monitoring, and automate responses to common problems. You will learn to integrate AIOps practices with existing DevOps workflows on AWS and utilize AWS AIOps services to predict and prevent application issues.
Automation Architect
An Automation Architect designs and implements automation solutions across various IT systems and processes. As an Automation Architect, knowing how to implement AI driven monitoring to reduce alarm fatigue and information overload is vital for making informed decisions and increasing productivity. You will also learn how integrating AIOps practices with existing DevOps workflows on AWS to predict and prevent application issues will help to streamline operations, improve the way to monitor applications, and automate responses to common problems. This course shows you how AIOps makes a huge difference.
Performance Engineer
Performance Engineers focus on optimizing the performance of systems and applications. This course is particularly relevant, as it addresses how AI can improve DevOps operational efficiency, directly impacting application performance. Knowing how to implement AI driven monitoring to reduce alarm fatigue and information overload allows a Performance Engineer to streamline operations, improve application monitoring, and automate responses to common problems. By learning how to integrate AIOps practices you will be able to predict and prevent application issues.
Cloud Architect
A Cloud Architect designs and implements cloud computing solutions, ensuring they are scalable, reliable, and secure. One of the main challenges to this role is to provide solutions to reduce operational incidents. This course helps show how to solve operational problems with machine learning techniques. You'll discover how to apply AI to DevOps processes, utilizing AWS AIOps services to predict and prevent application issues. This course will help you understand and implement AI driven monitoring to reduce alarm fatigue and information overload. A Cloud Architect will design and implement systems that benefit from the AIOps methodologies learned.
Systems Engineer
Systems Engineers are responsible for the design, implementation, and maintenance of IT systems. Information overload and lack of visibility is a primary concern for Systems Engineers in order to make informed decisions. This course teaches how AIOps can make a huge difference to improve how to monitor applications, and automate responses to common problems. Implementing AI driven monitoring helps to reduce alarm fatigue, making you a more effective Systems Engineer. By implementing AIOps practices within existing DevOps workflows enables you to predict and prevent application issues.
Solutions Architect
Solutions Architects design and implement complex IT solutions that meet specific business needs. This course will help you learn how to integrate AI into DevOps to improve operational efficiency. This enhances the solutions you design. You will also cover how to apply AI to DevOps processes, utilizing AWS AIOps services to predict and prevent application issues. Learning how to implement AI driven monitoring to reduce alarm fatigue and information overload promotes actionable information.
Technical Lead
Technical Leads guide and manage technical teams, ensuring projects are completed successfully. This course is helpful, as it provides insight into how AI can improve DevOps operational efficiency, a crucial aspect of managing a high-performing team. With your team now in production, knowing how to mitigate those challenges by integrating AIOps practices with existing DevOps workflows can help streamline operations, improve application monitoring, and automate responses to common problems. This course will give you tools to implement AI driven monitoring to reduce alarm fatigue. You will also know how to address the operational team.
Release Manager
A Release Manager is responsible for planning, scheduling, and controlling the software release process. This course can be particularly beneficial, as it focuses on how AI techniques can improve DevOps operational efficiency. This is important for managing and streamlining releases. You'll learn how to apply AI to DevOps processes and how to integrate AIOps practices with existing DevOps workflows. This course helps you understand how to utilize AWS AIOps services to predict and prevent application issues, reducing the number of operational incidents.
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models that solve real-world problems. This course may be useful, as it introduces the application of machine learning techniques to IT operations via AIOps. You will learn how to use machine learning to reduce operational incidents and improve applications. Implementing AI driven monitoring to reduce alarm fatigue and information overload will be directly relevant to building effective AIOps solutions. This course will help you understand how to derive actionable insights from operational data, a key task for this role.
Data Scientist
Data Scientists analyze data to extract insights and solve problems. This course may be useful as the use of machine learning techniques in solving operational problems may be helpful. You will learn how to take the vast quantities of data from IT operations and derive actionable insights to streamline operations, improve application monitoring, and automate responses to common problems. The course will help you in implementing AI driven monitoring to reduce alarm fatigue and information overload, which is essential for extracting valuable data.
Cloud Security Engineer
Cloud Security Engineers focus on securing cloud environments and protecting data. This course may be useful, as it introduces how AI can improve DevOps operational efficiency, which indirectly impacts security by improving monitoring and incident response. You'll learn how to apply AI to DevOps processes and how to integrate AIOps practices with existing DevOps workflows. The course may help you understand how to utilize AWS AIOps services to predict and prevent application issues. Learning how to implement AI driven monitoring to reduce alarm fatigue and information overload may also assist in threat detection.
IT Manager
An IT Manager oversees the IT department and ensures that IT systems and services meet the needs of the organization. This course may be helpful because it shows how AI techniques improve DevOps operational efficiency. IT managers will benefit from understanding how to apply AI to DevOps processes and how to integrate AIOps best practices within existing DevOps workflows. You will learn how to implement AI driven monitoring to reduce alarm fatigue and information overload.

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in DevOps and AI on AWS: AIOps.
Provides a comprehensive overview of AIOps principles and practices. It covers the application of machine learning to IT operations, aligning perfectly with the course's focus. It offers practical examples and case studies, enhancing the understanding of AIOps implementation in real-world scenarios. This book serves as a valuable reference for understanding the broader context of AIOps and its potential impact on DevOps workflows.
Provides a solid foundation in DevOps principles and practices. While not specifically focused on AIOps, it offers valuable context for understanding the broader DevOps landscape. It covers topics such as collaboration, automation, and continuous delivery, which are essential for successful AIOps implementation. This book is more valuable as additional reading to provide a broader understanding of DevOps.

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