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DevOps and AI on AWS

CI/CD for Generative AI Applications

Morgan Willis, Russell Sayers, and Rafael Lopes

In this course, we focus on the DevOps practices of building, deploying, and managing applications enhanced with generative AI features. You’ll learn how to implement Continuous Integration and Continuous Deployment (CI/CD) pipelines, explore strategies for reliable automation, and improve monitoring and observability for your applications. The course emphasizes practical skills to streamline releases, reduce potential errors, and maintain high-quality, scalable systems in dynamic cloud environments.

With dedicated modules for*Automatic deployments* , Infrastructure as Code, Monitoring, and Operations, you’ll improve your understanding and ability to execute as a Developer or DevOps Engineer. Get comfortable with AWS services by learning how to use Amazon CodeDeploy in a CI/CD pipeline and using the AWS Cloud Development Kit. You’ll then use AWS Services to help with observability and monitoring (Amazon CloudWatch Anomaly detection and AWS X-Ray insights) - both services with AI features to help with more effective monitoring and alarms. By the end of this course, you’ll have built a robust application that supports continuous releases, improves time to market for new features and fixes, and reduce potential for human error.

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What's inside

Learning objectives

Implement devops practices including automated builds, testing, and continuous integration pipelines.
Design and execute automatic deployments using amazon codedeploy in a ci/cd pipeline.
Develop infrastructure as code using aws cloudformation and aws cloud development kit.

Apply ai-enhanced monitoring and observability techniques using amazon cloudwatch anomaly detection and aws x-ray insights.
Demonstrate how devops and aiops practices improve continuous releases, time to market, and reduce human error in application development and operations.

Implement devops practices including automated builds, testing, and continuous integration pipelines.
Design and execute automatic deployments using amazon codedeploy in a ci/cd pipeline.
Develop infrastructure as code using aws cloudformation and aws cloud development kit.
Apply ai-enhanced monitoring and observability techniques using amazon cloudwatch anomaly detection and aws x-ray insights.
Demonstrate how devops and aiops practices improve continuous releases, time to market, and reduce human error in application development and operations.

Syllabus

Week 1: Introduction to DevOps

The Role of IaC in DevOps****

GenAI Considerations for DevOps

Hands-on Lab: Set Up CI/CD Pipeline

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on DevOps practices for building, deploying, and managing applications enhanced with generative AI features, which is highly relevant for modern software development

Teaches Infrastructure as Code using AWS CloudFormation and AWS Cloud Development Kit, which are essential skills for managing cloud infrastructure efficiently

Presented by Amazon Web Services, which is a leading provider of cloud computing services and a major player in the DevOps and AI space

Involves hands-on labs to set up CI/CD pipelines and deploy application infrastructure, which provides practical experience for learners

Requires familiarity with AWS services, so learners without prior experience may need to invest time in learning the basics first

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Reviews summary

Aws devops for genai applications

According to learners, this course offers a highly relevant and practical approach to applying DevOps principles and CI/CD pipelines specifically for generative AI applications on AWS. Students particularly value the hands-on labs and practical demonstrations that help solidify understanding of complex concepts and AWS services like CDK and CodeDeploy. Many find the integration of AIOps tools like CloudWatch and X-Ray useful for monitoring. While the course structure is generally clear, some learners note that prior AWS or DevOps experience is beneficial, and the labs can sometimes be challenging or require careful setup. Overall, it's seen as a strong course for professionals looking to build robust, continuously deployed GenAI systems on AWS.

Lectures provide clear explanations of core concepts.

"The instructors explain the concepts clearly, making it easy to understand the purpose and function of each part of the CI/CD pipeline."

"I found the video lectures well-structured and easy to follow. They effectively introduced the AWS services and their roles."

"The theoretical parts combined with the practical demos made for a comprehensive learning experience."

Course teaches useful AWS services for CI/CD and monitoring.

"Learning how to use AWS CodeDeploy and CDK within a CI/CD context was extremely helpful. These are essential tools for AWS professionals."

"The modules on CloudWatch Anomaly Detection and AWS X-Ray Insights were insightful. Applying AI-enhanced monitoring is a powerful concept."

"I gained practical experience with essential AWS services like CodeDeploy, CDK, CloudWatch, and X-Ray. This is very beneficial for my career."

"Understanding how to leverage Systems Manager for diagnostics and CloudTrail for monitoring activities was also a valuable part of the course."

Course covers highly relevant topics at the intersection of AI and DevOps.

"The combination of DevOps, AI, and AWS CI/CD is highly relevant to my current work. This course is very timely and addresses a real need."

"I found the focus on CI/CD for Generative AI applications particularly valuable. It's a niche but growing area, and this course fills that gap."

"Great content covering modern practices for deploying AI workloads on AWS. Directly applicable to industry trends."

"This course connects the dots between developing AI applications and deploying them reliably using standard DevOps practices."

Hands-on labs are a highlight, providing practical experience.

"The hands-on labs were incredibly useful for applying the concepts learned in the lectures. Building the CI/CD pipeline step-by-step solidified my understanding."

"I really appreciated the practical focus, especially the labs using CDK and CodeDeploy. They gave me real-world experience I can use."

"The labs are the core strength; they take you through setting up infrastructure and deployment pipelines on AWS, which is exactly what I needed."

"Learning by doing with the labs was the best part; it made the theory much clearer and showed how things work in practice on AWS."

Occasional difficulties reported with the lab environment.

"Encountered a few issues with the lab environment setup that took time to troubleshoot, which interrupted the flow."

"The labs were great when they worked, but I ran into permissions errors and resource provisioning problems a couple of times."

"Sometimes the instructions for the labs could be clearer, especially regarding prerequisites or potential issues that might arise."

Some prior experience with AWS or DevOps concepts is recommended.

"While the course is good, I felt I needed some prior background in AWS and general DevOps principles to keep up with the pace and complexity of the labs."

"If you're completely new to AWS or CI/CD, you might find parts of this course challenging. It builds on existing knowledge."

"I struggled a bit with some of the lab setups, which seemed to assume a certain level of familiarity with AWS CLI or cloud environments."

"It's definitely not a beginner course for cloud or DevOps. Having some foundation helps a lot."

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: CI/CD for Generative AI Applications with these activities:

Review AWS Fundamentals

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Reinforce your understanding of core AWS services and concepts before diving into DevOps and AI-specific implementations.

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Review the AWS Certified Cloud Practitioner exam guide.
Complete a foundational AWS training course on AWS Skill Builder.
Familiarize yourself with the AWS Management Console.

Brush Up on Python Scripting

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Strengthen your Python scripting skills, which are essential for automating tasks and managing infrastructure in a DevOps environment.

Browse courses on Python Scripting

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Complete a Python tutorial focused on automation.
Practice writing scripts to interact with AWS services using boto3.
Review common Python libraries used in DevOps.

Automate Infrastructure Provisioning with Terraform

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Gain hands-on experience with Infrastructure as Code (IaC) by automating the provisioning of AWS resources using Terraform.

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Set up a Terraform environment and configure AWS credentials.
Write Terraform configurations to provision EC2 instances and VPCs.
Automate the deployment of a simple application using Terraform.

Four other activities

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Document Your CI/CD Pipeline

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Solidify your understanding of CI/CD pipelines by documenting the steps involved in building, testing, and deploying your application.

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Create a diagram illustrating the CI/CD pipeline stages.
Write detailed descriptions of each stage, including tools and configurations.
Explain how the pipeline integrates with AWS services.

Explore AWS AI/ML Services

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Deepen your knowledge of AI/ML services on AWS by following guided tutorials and exploring practical use cases.

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Complete tutorials on Amazon SageMaker for model building and deployment.
Explore Amazon Comprehend for natural language processing.
Experiment with Amazon Rekognition for image and video analysis.

Build a Monitoring Dashboard with CloudWatch

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Enhance your monitoring and observability skills by creating a custom dashboard in CloudWatch to visualize key metrics and logs.

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Define the key metrics to monitor for your application.
Configure CloudWatch alarms to trigger notifications based on metric thresholds.
Create a dashboard to visualize metrics and logs in real-time.

Contribute to an Open Source DevOps Project

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Gain practical experience and contribute to the DevOps community by contributing to an open-source project related to CI/CD or infrastructure automation.

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Identify an open-source DevOps project on GitHub.
Review the project's documentation and contribution guidelines.
Contribute a bug fix, feature enhancement, or documentation update.

Career center

Learners who complete DevOps and AI on AWS: CI/CD for Generative AI Applications will develop knowledge and skills that may be useful to these careers:

DevOps Engineer

A DevOps Engineer bridges the gap between development and operations, ensuring smooth and efficient software releases. This role involves automating processes, managing infrastructure, and ensuring the reliability and scalability of systems. The 'DevOps and AI on AWS' course directly prepares you for this career by teaching you how to implement Continuous Integration and Continuous Deployment pipelines, a critical skill for any DevOps Engineer. Given the course focuses on Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit, it builds a solid foundation for managing infrastructure programmatically. Also, the course's emphasis on automating deployments and monitoring applications with AI features makes it especially relevant. Learning how to use Amazon CodeDeploy in a CI/CD pipeline and using AWS services like CloudWatch for anomaly detection gives a prospective DevOps Engineer a competitive advantage.

See salaries and explore the career path for DevOps Engineer

Cloud Engineer

Cloud Engineers are responsible for designing, building, and maintaining cloud infrastructure and services. They need a strong understanding of cloud platforms, automation, and infrastructure management. This 'DevOps and AI on AWS' course will be very helpful because it focuses on using AWS services for CI/CD pipelines and infrastructure as code. The hands on labs in the course, such as the one focused on deploying application infrastructure with CDK using a CI/CD pipeline, will give you practical skills and experience. Furthermore, the course's focus on monitoring and observability using Amazon CloudWatch and AWS X-Ray will help Cloud Engineers build and maintain healthy and reliable cloud environments. Given that Cloud Engineers will be expected to keep up to date on the latest AI developments, this course will be of particular value in that regard.

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Automation Engineer

Automation Engineers design and implement automated systems and processes to improve efficiency and reduce errors. This often involves scripting, configuration management, and the use of CI/CD pipelines. The 'DevOps and AI on AWS' course is directly relevant to this role, as it teaches how to implement CI/CD pipelines for automated deployments using Amazon CodeDeploy. The course's focus on Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit is also crucial for automation engineers, as it allows them to write code and automate the provisioning and management of infrastructure resources. The course will also be useful because it covers AI enhanced monitoring and observability techniques, helping an Automation Engineer ensure the health and performance of their systems.

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Site Reliability Engineer

Site Reliability Engineers focus on ensuring the reliability, availability, and performance of systems. They use automation, monitoring, and incident response to keep systems running smoothly. The 'DevOps and AI on AWS' course will be helpful for SREs because it will teach you how to implement CI/CD pipelines, automate deployments, and monitor applications using AI enhanced techniques. The course's focus on Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit will also be helpful because it allows SREs to manage infrastructure programmatically and ensure consistency. The course will also be useful as it teaches you how to use Amazon CloudWatch and AWS X-Ray to monitor and observe applications, which is essential for identifying and resolving issues quickly.

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Infrastructure Engineer

Infrastructure Engineers design, build, and maintain the underlying infrastructure that supports applications. This includes servers, networks, storage, and databases. The 'DevOps and AI on AWS' course will be useful for Infrastructure Engineers because it focuses on Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit. This will allow you to manage infrastructure programmatically and automate the provisioning and configuration of resources. Additionally, the course's coverage of CI/CD pipelines and automated deployments will help ensure that infrastructure changes are deployed smoothly and efficiently. As Infrastructure Engineers must also consider monitoring and observability, the AI enhanced strategies covered in the course will be directly applicable.

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Release Manager

Release Managers are responsible for planning, coordinating, and managing the release of software updates and new features. The 'DevOps and AI on AWS' course is directly applicable to this role. It teaches how to implement Continuous Integration and Continuous Deployment pipelines, which are essential for automating and streamlining the release process. The course's focus on automated deployments using Amazon CodeDeploy and Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit will be helpful because it will allow you to automate the release process and reduce the risk of errors. A Release Manager should be comfortable with AI enhanced monitoring, as taught in the course, so that they can better oversee the operations of their software releases.

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Systems Administrator

Systems Administrators are responsible for the day to day operation and maintenance of computer systems and servers. This involves tasks such as installing software, configuring hardware, and troubleshooting issues. The 'DevOps and AI on AWS' course is useful for Systems Administrators because it explores automation, Infrastructure as Code, and cloud monitoring, all key skills for modern systems administration. The course's focus on using AWS services like Amazon CodeDeploy and CloudWatch will make you more effective at managing systems in the cloud. A Systems Administrator can leverage the AWS Cloud Development Kit covered in the course to manage and maintain their infrastructure. Additionally, understanding AI enhanced monitoring techniques will aid in quickly identifying and resolving issues.

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Solutions Architect

Solutions Architects design and implement cloud based solutions that meet specific business needs. They need a broad understanding of cloud platforms, architecture patterns, and DevOps practices. While a master's degree is not required, it is commonly held. The 'DevOps and AI on AWS' course may be useful as it provides a strong foundation in DevOps practices on AWS, including CI/CD pipelines, Infrastructure as Code, and monitoring. The course's focus on automated deployments using Amazon CodeDeploy and the AWS Cloud Development Kit would also be useful in helping you design and implement automated and scalable solutions. Furthermore, the course will also assist with AI enhanced monitoring and observability techniques using Amazon CloudWatch and AWS X-Ray, which become increasingly important in modern cloud architectures.

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Software Developer

Software Developers write and maintain code for applications. While seemingly distinct, understanding DevOps principles and tools can significantly enhance a developer's ability to contribute to a team and deliver high quality software. The 'DevOps and AI on AWS' course will be useful for Developers because it covers CI/CD pipelines, automated deployments, and monitoring techniques. A better understanding of how software is built, tested, and released helps developers write more robust and maintainable code. In particular, the course's focus on using Amazon CodeDeploy in a CI/CD pipeline and the AWS Cloud Development Kit will demonstrate the Developer's role in the deployment process. Moreover, the knowledge of AI enhanced monitoring and observability gained from this course can help you write code that is easier to monitor and troubleshoot.

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Technical Project Manager

Technical Project Managers oversee and coordinate technical projects, ensuring they are completed on time and within budget. While such a role might call for a master's degree, it is not always the case. Although this course may be useful, it is unlikely to be the central focus of your preparations for such a position. The 'DevOps and AI on AWS' course will be useful for Technical Project Managers because it provides an understanding of DevOps practices, CI/CD pipelines, and cloud technologies. This understanding will help you better manage technical projects and communicate effectively with technical teams. The course's focus on automation and Infrastructure as Code may be helpful because it allows you to understand the technical challenges involved in automating and managing infrastructure. Given the increasing use of AI in technology, such as with the course's AI enhanced monitoring focus, you will be better prepared to manage related projects.

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Data Engineer

Data Engineers build and maintain the infrastructure for data storage, processing, and analysis. While the course may not directly focus on data engineering tasks, understanding DevOps principles and cloud technologies is increasingly important in this field. The 'DevOps and AI on AWS' course may be useful for Data Engineers because it covers CI/CD pipelines, Infrastructure as Code, and cloud monitoring, all of which can be applied to data engineering workflows. The course's focus on using AWS services like Amazon CloudWatch can help you monitor and troubleshoot data pipelines, which is particularly important for Data Engineers. Furthermore, the AI enhanced features of Amazon CloudWatch may be relevant as data operations become increasingly sophisticated.

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Cloud Security Engineer

Cloud Security Engineers are responsible for securing cloud environments and protecting data from threats. The 'DevOps and AI on AWS' course may be useful for Cloud Security Engineers because it provides a foundation in AWS services and DevOps practices. While the course may not cover security topics directly, understanding how applications are deployed and managed in the cloud is essential for security. The course's focus on Infrastructure as Code using AWS CloudFormation and the AWS Cloud Development Kit will be helpful for security engineers because it allows them to automate security configurations and ensure consistency. CloudTrail, which is covered, helps with auditing, which is an important aspect of security.

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Database Administrator

Database Administrators manage and maintain databases, ensuring their availability, performance, and security. While the 'DevOps and AI on AWS' course isn't directly focused on database administration, understanding DevOps practices and cloud technologies can be valuable. The course may be useful for Database Administrators because it outlines Infrastructure as Code and cloud monitoring. Database infrastructure can be managed and automated using tools like AWS CloudFormation and the AWS Cloud Development Kit, covered in the course. Moreover, the AI enhanced features of Amazon CloudWatch, also examined in the course, assist with database performance monitoring.

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Network Engineer

Network Engineers design, implement, and maintain network infrastructure. While networking is not the primary focus, understanding cloud technologies and automation is increasingly valuable in network engineering. The 'DevOps and AI on AWS' course covers Infrastructure as Code, which can apply to network infrastructure automation. This will be useful because it allows you to manage network configurations and automate the deployment of changes. Although the course is not solely focused on networking, such understanding can inform the skills of a Network Engineer. Furthermore, the AI features of Amazon CloudWatch, covered in the course, can assist with network monitoring.

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Technical Recruiter

Technical Recruiters find, screen, and recruit qualified candidates for technical positions. Familiarity with the technologies and practices mentioned in the 'DevOps and AI on AWS' course allows you to better understand the skills and experience needed for these roles. After taking this course, you will be better equipped to communicate with hiring managers and candidates, assess their technical abilities, and find the best talent for these roles. You will also better understand what it takes to succeed in these positions, allowing you to identify promising candidates and make informed hiring decisions. Given that this role requires understanding a variety of career paths, this course may augment your knowledge.

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Reading list

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DevOps and AI on AWS

CI/CD for Generative AI Applications

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Aws devops for genai applications

Activities

Career center

Reading list

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