DevOps and AI on AWS: CI/CD for Generative AI Applications

DevOps and AI on AWS: CI/CD for Generative AI Applications

This course is part of DevOps and AI on AWS Specialization

Instructors: Morgan Willis

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3 modules

Gain insight into a topic and learn the fundamentals.

12 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

12 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Implement DevOps practices including automated builds, testing, and continuous integration pipelines.
Design and execute automatic deployments using Amazon CodeDeploy in a CI/CD pipeline.
Demonstrate how DevOps and AIOps practices improve continuous releases, time to market, and reduce human error in app development and operations.
Apply AI-enhanced monitoring and observability techniques using Amazon CloudWatch Anomaly Detection and AWS X-Ray Insights.

Details to know

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Assessments

5 assignments

Taught in English

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This course is part of the DevOps and AI on AWS Specialization

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There are 3 modules in this course

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.

This module introduces the fundamentals of DevOps and its role in modern software development. It covers key principles such as Continuous Integration (CI), Infrastructure as Code (IaC), and automation, providing a foundation for managing infrastructure efficiently. You will explore how DevOps integrates with generative AI workflows, addressing unique challenges like AI model testing and deployment.

What's included

11 videos9 readings2 assignments1 app item2 plugins

11 videosTotal 66 minutes

Introduction to the Course2 minutesPreview module
Introduction to DevOps6 minutes
Module 1 Introduction1 minute
The Role of IaC in DevOps6 minutes
The Code Phase6 minutes
GenAI Considerations for DevOps4 minutes
The Build10 minutes
The Test6 minutes
Testing Generative AI Applications5 minutes
Adding Tests to Continuous Integration7 minutes
Continuous Integration9 minutes

9 readingsTotal 62 minutes

Welcome to the course5 minutes
Pre-Course Survey1 minute
Course Roadmap5 minutes
Lab Maintenance in Progress10 minutes
DevOps and IaC10 minutes
GenAI considerations for DevOps10 minutes
Code build tools and services10 minutes
Continuous Integration10 minutes
Mid-Course Survey1 minute

2 assignmentsTotal 30 minutes

Module 1 Quiz10 minutes
Pre-Assessment20 minutes

1 app itemTotal 60 minutes

Set Up CI/CD Pipeline60 minutes

2 pluginsTotal 25 minutes

Pre-Course Survey10 minutes
Mid-Course Survey15 minutes

This module focuses on deployment strategies and automation in a DevOps pipeline. Learners gain hands-on insights into AWS CodeDeploy, AWS CloudFormation, and AWS CDK, understanding how to automate infrastructure provisioning and application releases. The module also explores best practices for reducing downtime, troubleshooting deployments, and ensuring smooth model rollouts in generative AI applications.

What's included

12 videos4 readings1 assignment1 app item1 discussion prompt

12 videosTotal 71 minutes

Module 2 Introduction2 minutesPreview module
Deployment Strategies for Amazon EC28 minutes
Understanding the AppSpec file4 minutes
Using AWS CodeDeploy in a Pipeline4 minutes
Troubleshooting Deployment Details and Errors4 minutes
CI/CD Pipelines for Infrastructure Components4 minutes
Introduction to AWS CloudFormation6 minutes
Reviewing an AWS CloudFormation Template7 minutes
AWS CloudFormation: How to Handle and Prevent Errors5 minutes
Troubleshooting AWS CloudFormation4 minutes
Using the AWS Cloud Development Kit7 minutes
Deploying Our Application Stack Using the AWS CDK10 minutes

4 readingsTotal 45 minutes

Deployment Strategies for Serverless10 minutes
AWS CloudFormation Extended Features15 minutes
Troubleshooting AWS CloudFormation10 minutes
AWS Cloud Development Kit10 minutes

1 assignmentTotal 10 minutes

Module 2 Quiz10 minutes

1 app itemTotal 60 minutes

Deploy Application Infrastructure Using CDK With a CI/CD Pipeline60 minutes

1 discussion promptTotal 10 minutes

Automating Deployments with DevOps10 minutes

This module explores the importance of monitoring, observability, and operational management in DevOps workflows. Learners discover how to use Amazon CloudWatch, AWS CloudTrail, AWS X-Ray, and AWS Systems Manager to track application performance, detect issues, and ensure infrastructure stability. Special focus is given to observability in generative AI applications, highlighting metrics, logging, and automated response strategies to maintain system reliability.