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DevOps and AI on AWS

Upgrading Apps with Generative AI

Russell Sayers, Rafael Lopes, and Morgan Willis

Explore the intersection of DevOps and Generative AI on AWS in this hands-on course. Learn to enhance existing applications with powerful AI features using Amazon Bedrock's large language models (LLMs). You'll gain practical experience in implementing customized text generation, mastering prompt engineering, and applying advanced techniques like fine-tuning and Retrieval Augmented Generation (RAG).

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Explore the intersection of DevOps and Generative AI on AWS in this hands-on course. Learn to enhance existing applications with powerful AI features using Amazon Bedrock's large language models (LLMs). You'll gain practical experience in implementing customized text generation, mastering prompt engineering, and applying advanced techniques like fine-tuning and Retrieval Augmented Generation (RAG).

We focus on essential DevOps practices, guiding you through the process of coding, building, and testing an application upgraded with generative AI capabilities. You'll learn to integrate these AI features seamlessly into your software development lifecycle, ensuring smooth deployment and maintenance.

The course also sharpens crucial developer skills. We highlight how to enable effective source control management, enabling efficient collaboration and version control in your projects. Additionally, you'll master unit testing to ensure the reliability of your applications.

By the end of this course, you'll be equipped to modernize applications using generative AI, implement proven DevOps practices, and leverage AWS' AI services. Whether you're a developer expanding your AI expertise or a DevOps professional incorporating AI technologies, this course provides the tools to excel in the evolving landscape of software development and artificial intelligence.

Join us to transform your applications and development practices with the power of generative AI on AWS.

Enroll now

What's inside

Learning objectives

  • Understanding observability and its importance in application development
  • Key components of aiops, including anomaly detection, predictive analysis, automated root cause analysis, and remediation
  • Practical use cases for aiops in real-world scenarios
  • How to apply ai and machine learning to automate it operations tasks

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on integrating AI features into the software development lifecycle, which is essential for modernizing applications and improving deployment and maintenance
Explores the intersection of DevOps and Generative AI on AWS, which is highly relevant for those looking to leverage cloud-based AI services
Teaches prompt engineering, fine-tuning, and Retrieval Augmented Generation (RAG), which are advanced techniques for customizing text generation
Requires familiarity with AWS services, which may necessitate additional learning for those new to the Amazon Web Services ecosystem
Highlights source control management and unit testing, which are crucial developer skills for ensuring collaboration and application reliability

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Reviews summary

Devops and ai integration on aws

According to learners, this course offers a solid introduction to integrating DevOps and AI on AWS, particularly focusing on upgrading existing applications with generative AI. Many found the hands-on labs and practical exercises to be particularly valuable for applying concepts like Amazon Bedrock, RAG, and prompt engineering. However, some students noted that the depth of coverage varied, suggesting that certain advanced topics might feel rushed or require prior knowledge. A few reviewers also mentioned facing minor technical issues with lab environments. Overall, students report gaining actionable skills, though potential learners should be prepared for a focus on integrating GenAI into existing workflows rather than a deep dive into AI fundamentals or advanced DevOps strategies.
Provides a good starting point for combining fields.
"Got a good overview of how GenAI fits into a DevOps workflow."
"This course gave me a solid starting point to explore AI on AWS."
"It was a clear introduction to combining these two important fields."
Hands-on exercises solidify understanding.
"The labs were very useful for actually trying out Bedrock."
"I really appreciated the practical exercises, they made the concepts click."
"Building that generative AI feature step-by-step in the lab was the highlight."
Some users faced difficulties with lab setups.
"Ran into a few errors setting up the labs, needed some troubleshooting."
"The lab environment was a bit tricky to get working initially."
"Encountered some unexpected issues during the hands-on parts."
Some topics could use more detailed coverage.
"Felt like some complex topics were just skimmed over."
"Wish there was more detail on fine-tuning or advanced RAG techniques."
"Some parts were a bit basic, others seemed to assume more background than I had."

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: Upgrading Apps with Generative AI with these activities:
Review AWS Fundamentals
Reviewing AWS fundamentals ensures a solid understanding of the cloud environment where DevOps and AI tools will be deployed.
Show steps
  • Review AWS core services like EC2, S3, and IAM.
  • Familiarize yourself with the AWS Management Console.
  • Complete a basic AWS tutorial or lab.
Brush Up on Python Programming
Refreshing Python skills is crucial for scripting, automation, and interacting with AI services in the course.
Browse courses on Python Programming
Show steps
  • Review Python syntax and data structures.
  • Practice writing scripts for automation tasks.
  • Explore Python libraries commonly used in DevOps and AI.
Read 'Effective DevOps' by Jennifer Davis and Ryn Daniels
Reading 'Effective DevOps' will provide a strong foundation for understanding the DevOps aspects of the course.
View Effective DevOps on Amazon
Show steps
  • Read the book, focusing on the core principles of DevOps.
  • Take notes on key concepts and practices.
  • Relate the concepts to the course syllabus.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow AWS AI/ML Tutorials
Following AWS AI/ML tutorials will provide hands-on experience with the AI services used in the course.
Show steps
  • Find tutorials on Amazon Bedrock and other AI services.
  • Follow the tutorials step-by-step.
  • Experiment with different parameters and configurations.
Build a Simple AI-Powered Application
Building a simple AI-powered application will solidify your understanding of the concepts covered in the course.
Show steps
  • Choose a simple application idea that can be enhanced with AI.
  • Integrate an AWS AI service like Amazon Bedrock.
  • Deploy the application to AWS.
Write a Blog Post on DevOps and AI
Writing a blog post will help you synthesize your knowledge and share it with others.
Show steps
  • Choose a specific topic related to DevOps and AI.
  • Research the topic thoroughly.
  • Write a clear and concise blog post.
  • Publish the blog post on a platform like Medium or LinkedIn.
Read 'Building Machine Learning Powered Applications' by Emmanuel Ameisen
Reading 'Building Machine Learning Powered Applications' will provide valuable insights into the AI aspects of the course.
View Building Machine Learning Powered Applications:... on Amazon
Show steps
  • Read the book, focusing on the practical aspects of building and deploying ML applications.
  • Take notes on key concepts and practices.
  • Relate the concepts to the course syllabus.

Career center

Learners who complete DevOps and AI on AWS: Upgrading Apps with Generative AI will develop knowledge and skills that may be useful to these careers:
AIOps Engineer
An AIOps Engineer specializes in using artificial intelligence to automate and improve IT operations. This role involves applying machine learning techniques to monitor systems, detect anomalies, and predict potential issues. Since the course focuses on how to apply AI and machine learning to automate IT operations tasks, an aspiring AIOps Engineer will benefit. This course helps to build a foundation and equip the AIOps Engineer to modernize applications using generative AI. The DevOps knowledge gained from the course helps the AIOps engineer deploy the AI models and tools effectively.
See salaries and explore the career path for AIOps Engineer
Generative AI Specialist
A Generative AI Specialist focuses on developing and implementing applications using generative AI models. The course provides direct experience in implementing customized text generation, mastering prompt engineering, and applying advanced techniques like fine-tuning and Retrieval Augmented Generation. This knowledge directly translates to the skills needed to excel as a Generative AI Specialist, working with tools like Amazon Bedrock's large language models. The course's hands-on approach and focus on practical applications could be highly valuable for someone specializing in this rapidly growing field.
See salaries and explore the career path for Generative AI Specialist
AI Operations Engineer
An AI Operations Engineer specializes in the deployment, monitoring, and maintenance of AI models and systems within a production environment. A course focused on DevOps and generative AI is directly relevant, as it equips the engineer with the skills to integrate AI features into existing applications and ensure their smooth operation. The hands-on experience with Amazon Bedrock's LLMs, prompt engineering, and advanced techniques like RAG is highly valuable. AI Operations Engineers can use the DevOps practices taught in the course to effectively manage the lifecycle of AI applications, ensuring their reliability and scalability. The course helps to build a foundation with AIOps.
See salaries and explore the career path for AI Operations Engineer
DevOps Engineer
A DevOps Engineer focuses on automating and streamlining the software development lifecycle, ensuring smooth and efficient deployment of applications. The core of this course focuses on essential DevOps practices, guiding you through the process of coding, building, and testing an application upgraded with generative AI capabilities. You will learn to integrate AI features seamlessly into the software development lifecycle, ensuring smooth deployment and maintenance, which directly aligns with the responsibilities of a DevOps Engineer. The course also highlights how to enable effective source control management, enabling efficient collaboration and version control in projects. This course may be useful to understand AIOps.
See salaries and explore the career path for DevOps Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This often involves coding, testing, and debugging software. This course helps build a foundation in modern software development practices by integrating generative AI capabilities into existing applications. The hands-on experience with Amazon Bedrock and the focus on practical applications of large language models expands expertise in AI-driven software development. Software Engineers will appreciate the course's emphasis on source control management and unit testing, essential skills for ensuring the reliability and maintainability of their code. This course may also be useful to understand AIOps.
See salaries and explore the career path for Software Engineer
AI Application Developer
An AI Application Developer designs, develops, and implements applications that leverage artificial intelligence technologies. This role involves understanding AI algorithms, machine learning models, and how to integrate these with existing software systems. This course helps the learner develop skills in modernizing applications using generative AI, a core aspect of the AI Application Developer's work. The hands-on experience with Amazon Bedrock and the focus on practical applications of large language models will facilitate entry into the field. Learning techniques like prompt engineering and Retrieval Augmented Generation may be useful to an AI Application Developer. The course's DevOps components also contribute to the smooth deployment and maintenance of AI applications.
See salaries and explore the career path for AI Application Developer
Machine Learning Engineer
A Machine Learning Engineer focuses on building and deploying machine learning models, working at the intersection of software engineering and data science. This role requires a strong understanding of algorithms, data structures, and software development practices. A course that focuses on integrating generative AI into existing applications helps build a foundation in applying machine learning techniques to real-world problems. In particular, learning to use Amazon Bedrock's large language models and implementing techniques like fine-tuning and Retrieval Augmented Generation directly benefit a Machine Learning Engineer. The course's focus on DevOps practices also ensures the engineer can effectively deploy and maintain these AI-driven applications. It may be useful to understand AIOps.
See salaries and explore the career path for Machine Learning Engineer
Software Architect
A Software Architect designs the overall structure and components of software systems. This role requires a deep understanding of software development principles, architectural patterns, and technology trends. This course helps to build a foundation in how to modernize applications using generative AI. It also includes the DevOps skills needed to integrate AI into the software development lifecycle seamlessly. The hands-on experience with Amazon Bedrock will be beneficial for this role. Often, this role requires an advanced degree.
See salaries and explore the career path for Software Architect
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud-based solutions, often involving complex architectures and integrations. This role requires a broad understanding of cloud services, networking, and security. A course that explores the intersection of DevOps and Generative AI on AWS can significantly assist a Cloud Solutions Architect with the design of cloud solutions that incorporate AI capabilities. The practical experience of enhancing applications with Amazon Bedrock's large language models would broaden expertise in AI services on AWS. The DevOps practices and focus on source control management are extremely relevant for architecting and deploying scalable cloud solutions. This course may also be useful to understand AIOps.
See salaries and explore the career path for Cloud Solutions Architect
Solutions Architect
A Solutions Architect designs and implements technical solutions to meet specific business needs. This role often requires a broad understanding of various technologies and the ability to integrate them effectively. A hands-on course covering Generative AI on AWS helps the Solutions Architect design solutions incorporating modern AI capabilities. The practical experience gained in upgrading applications with Amazon Bedrock's large language models translates directly to real-world problem solving. The course's focus on DevOps practices ensures that the architect understands the deployment and maintenance aspects of the solutions they design. This course may be useful to understand AIOps.
See salaries and explore the career path for Solutions Architect
Cloud Engineer
A Cloud Engineer builds, deploys, and manages applications and services in the cloud. This role requires a deep understanding of cloud platforms, automation tools, and DevOps practices. This course, which focuses on using generative AI on AWS, helps build a foundation for Cloud Engineers who want to incorporate AI capabilities into their cloud deployments. The hands-on experience with Amazon Bedrock's large language models provides practical skills in leveraging AI services. The course's emphasis on DevOps practices, source control management, and unit testing aligns directly with the responsibilities of a Cloud Engineer. This course may be useful to understand AIOps.
See salaries and explore the career path for Cloud Engineer
Technical Lead
A Technical Lead guides a team of developers in the design, development, and implementation of software projects. This role requires strong technical skills, leadership abilities, and a broad understanding of software development practices. A course focused on DevOps and generative AI on AWS would be highly suitable for a Technical Lead, particularly one overseeing projects that involve AI-driven applications. The practical experience of enhancing applications with Amazon Bedrock's large language models would enhance technical expertise and help the Technical Lead make informed decisions. The DevOps practices and emphasis on source control management are essential for leading a successful development team. This course may be useful to understand AIOps.
See salaries and explore the career path for Technical Lead
Data Scientist
A Data Scientist analyzes data to extract insights and develop predictive models. While this role often involves statistical analysis and algorithm development, understanding how to deploy and integrate these models into applications is increasingly important. This course, which explores the intersection of DevOps and Generative AI on AWS, helps to build a foundation for Data Scientists looking to operationalize their models. The practical experience of enhancing applications with Amazon Bedrock's large language models would be a valuable skill. The focus on DevOps practices ensures that the Data Scientist can effectively collaborate with engineering teams to deploy and maintain AI-driven applications. This course may be useful to understand AIOps.
See salaries and explore the career path for Data Scientist
Full-Stack Developer
A Full Stack Developer works on both the front-end and back-end of web applications. This role requires a broad range of skills, including programming, database management, and server administration. This course may be useful for Full Stack Developers who want to expand their skills to include AI-powered features. The course helps build a foundation in integrating generative AI into existing applications, providing the knowledge needed to add intelligent capabilities to web applications. The focus on source control management and unit testing are also valuable skills for any Full Stack Developer. This course may be useful to understand AIOps.
See salaries and explore the career path for Full-Stack Developer
AI Product Manager
An AI Product Manager defines the strategy, roadmap, and features for AI-powered products. While this role is less technical than engineering roles, a solid understanding of AI technologies is crucial for making informed decisions. A course that explores the intersection of DevOps and Generative AI on AWS may be useful for AI Product Managers, who may want to understand the capabilities and limitations of AI technologies. The hands-on experience with Amazon Bedrock's large language models can provide valuable insights into the practical applications of AI. This course may be useful to understand AIOps.
See salaries and explore the career path for AI Product Manager

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: Upgrading Apps with Generative AI.
Cover image
Effective DevOps
Save
Provides a comprehensive overview of DevOps principles and practices. It covers topics such as collaboration, automation, and continuous delivery. Reading this book will provide a strong foundation for understanding the DevOps aspects of the course. It is commonly used as a reference by industry professionals.
Effective DevOps
Paperback
Check
Effective DevOps
Kindle Edition
Check
Cover image
Building Machine Learning Powered Applications
Save
Focuses on the practical aspects of building and deploying machine learning applications. It covers topics such as data pipelines, model deployment, and monitoring. Reading this book will provide valuable insights into the AI aspects of the course. It is particularly useful as additional reading to expand on the course materials.
Building Machine Learning Powered Applications:...
Paperback
$$
Building Machine Learning Powered Applications:...
Kindle Edition
$$

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Effort
2 to 4 hours per week for 2 weeks
Level
Introductory
Via
edX
Institution
Amazon Web Services
Instructors
Russell Sayers
Rafael Lopes
Morgan Willis
Session
March 19, 2025 - June 28, 2026
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Focuses on integrating AI features into the software development lifecycle, which is essential for modernizing applications and improving deployment and maintenance
Explores the intersection of DevOps and Generative AI on AWS, which is highly relevant for those looking to leverage cloud-based AI services
Teaches prompt engineering, fine-tuning, and Retrieval Augmented Generation (RAG), which are advanced techniques for customizing text generation
Requires familiarity with AWS services, which may necessitate additional learning for those new to the Amazon Web Services ecosystem
Highlights source control management and unit testing, which are crucial developer skills for ensuring collaboration and application reliability
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