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

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

This course is part of DevOps and AI on AWS Specialization

Instructors: Russell Sayers

2,165 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Beginner level

Recommended experience

1 week to complete

at 10 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

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

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videos8 readings2 assignments1 app item2 plugins

11 videosTotal 67 minutes

Introduction to the Course3 minutes
Introduction to DevOps6 minutes
Module 1 Introduction1 minute
The Role of IaC in DevOps7 minutes
The Code Phase6 minutes
GenAI Considerations for DevOps4 minutes
The Build10 minutes
The Test7 minutes
Testing Generative AI Applications6 minutes
Adding Tests to Continuous Integration7 minutes
Continuous Integration9 minutes

8 readingsTotal 52 minutes

Welcome to the course5 minutes
Pre-Course Survey1 minute
Course Roadmap5 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 item

12 videosTotal 72 minutes

Module 2 Introduction3 minutes
Deployment Strategies for Amazon EC28 minutes
Understanding the AppSpec file4 minutes
Using AWS CodeDeploy in a Pipeline4 minutes
Troubleshooting Deployment Details and Errors5 minutes
CI/CD Pipelines for Infrastructure Components4 minutes
Introduction to AWS CloudFormation6 minutes
Reviewing an AWS CloudFormation Template8 minutes
AWS CloudFormation: How to Handle and Prevent Errors6 minutes
Troubleshooting AWS CloudFormation5 minutes
Using the AWS Cloud Development Kit8 minutes
Deploying Our Application Stack Using the AWS CDK11 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

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.