DevOps and AI on AWS

Amazon Web Services via edX Professional Certificate

Overview

DevOps is transformative for organizations of any size. It’s ability to aid quick development cycles and more frequent releases, allows companies to bring features and benefits to customers faster. Additionally, DevOps improves collaboration, increases efficiencies, leading to lower costs and happier customers. Now, with the advent of Generative AI, DevOps works synergistically with AI to enhance IT operations and software delivery.

DevOps and AI on AWS is three course specialization program that will take through each phase of the DevOps process, helping you grasp the general concepts and guiding you to create solutions using AWS services, including services that leverage Generative AI technologies. At the specialization’s conclusion, you will have created a continuous integration and continuous delivery (or CICD pipeline).

Syllabus

Courses under this program:
Course 1: DevOps and AI on AWS: Upgrading Apps with Generative AI

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).

Course 2: DevOps and AI on AWS: CI/CD for Generative AI Applications

In this course, we explore 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.

Course 3: DevOps and AI on AWS: AIOps

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.

Courses

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2 weeks, 2-4 hours a week, 2-4 hours a week
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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.
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3 weeks, 2-4 hours a week, 2-4 hours a week
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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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2 weeks, 2-4 hours a week, 2-4 hours a week
View details

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.