EdX

Cloud Computing Foundations (edX)

Offered by AI (Pragmatic AI Labs),
Cloud Computing Foundations (edX)

Learn the foundations of cloud computing and build websites using serverless, PaaS, and IaaS technologies. Apply DevOps principles and create continuous delivery pipelines for efficient cloud infrastructure management.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

In this course, you will:
Build foundational cloud computing infrastructure, including websites using serverless technology, virtual machines, and PaaS (Platform as a Service).
Apply agile software development techniques to small and large projects, useful for building portfolio projects and global-scale cloud infrastructures.
Learn how to effectively choose the right level of abstraction: IaaS (Infrastructure as a Service), MaaS (Metal as a Service), PaaS, and Serverless.
Apply DevOps principles to Cloud Computing, Data Engineering, and Machine Learning.
Utilize IaC (Infrastructure as Code) to manage and provision Cloud infrastructure in a repeatable and idempotent process.
Develop Continuous Delivery pipelines for efficient cloud infrastructure management.
Evaluate best practices for implementing solutions with Cloud Computing.
This course is ideal for beginners and intermediate students interested in applying cloud computing to data science, machine learning, and data engineering. Students should have beginner-level Linux and Python skills.
This course is part of the Introduction to Cloud Computing Professional Certificate.

What you'll learn

  • Build websites using serverless, PaaS, and IaaS technologies
  • Apply DevOps principles to cloud computing
  • Utilize Infrastructure as Code (IaC) for cloud management
  • Develop Continuous Delivery pipelines for efficient infrastructure management
  • Evaluate and choose appropriate cloud service models
  • Apply agile software development techniques to cloud projects
  • Effectively communicate in technical discussions and project management

Syllabus

Here is the course structure formatted with bullets for each module:

1. Module 1: Getting Started with Cloud Computing Foundations (1 hour)

  • Videos:
  • Instructor Introduction (1 minute) [Preview module]
  • Course Introduction (1 minute)
  • Course Prerequisites (2 minutes)
  • Lab Onboarding (1 minute)
  • Course 1 Project Overview (1 minute)
  • Readings:
  • Course Structure and Discussion Etiquette (10 minutes)
  • Getting Started and Course Gotchas (10 minutes)
  • Create a free account with AWS, Azure and GCP (30 minutes)
  • Specialization Project Roadmap: Course 1 (10 minutes)
  • Quiz:
  • Confirming Free Tier Cloud Accounts (30 minutes)
  • Discussion Prompt:
  • Introductions (10 minutes)

2. Module 2: Developing Effective Technical Communication (7 hours)

  • Videos:
  • Introduction to Technical Discussions (1 minute) [Preview module]
  • Technical Discussions with Markdown, GitHub and Jupyter/Colab (10 minutes)
  • Creating Technical Demo Videos (1 minute)
  • Effective Critical Thinking (5 minutes)
  • Effective Technical Triple Threat (2 minutes)
  • Introduction to Effective Technical Teamwork (0 minutes)
  • Effective Technical Teamwork (6 minutes)
  • Introduction to Technical Project Management (2 minutes)
  • Effective Technical Project Management (5 minutes)
  • Ticket Tracking with Trello (4 minutes)
  • Project Planning with Spreadsheets (5 minutes)
  • Project Management Anti-Patterns (5 minutes)
  • Readings:
  • Key Terms (10 minutes)
  • Effective Technical Discussions (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • Lesson Reflections (10 minutes)
  • Key Terms (10 minutes)
  • Effective Technical Project Management (10 minutes)
  • Lesson Reflection (10 minutes)
  • Project Plan for Course 1 Project (10 minutes)
  • Quiz:
  • Effective Technical Communication Quiz (20 minutes)
  • Assignments:
  • Quiz-Effective Technical Project Management (30 minutes)
  • Quiz-Effective Technical Teamwork (30 minutes)
  • Quiz-Effective Technical Project Management (30 minutes)
  • Discussion Prompts:
  • Reproducible Technical Discussion (10 minutes)
  • Team Performance Analysis (10 minutes)
  • Agile vs. Waterfall Planning (10 minutes)
  • Course 1 Project Plan (60 minutes)
  • Ungraded Labs:
  • Create Markdown in Jupyter (60 minutes)
  • Unit Testing (60 minutes)

3. Module 3: Exploring Cloud Onboarding (9 hours)

  • Videos:
  • Introduction to AWS Cloud Development (5 minutes) [Preview module]
  • Introduction to Continuous Integration (4 minutes)
  • Cloud Development with AWS Cloud9 (9 minutes)
  • Constructing a Python Project Scaffold (19 minutes)
  • Introduction to GitHub Actions (8 minutes)
  • Setup Amazon CodeCatalyst (5 minutes)
  • CodeWhisperer Natural Language to Bash CLI (3 minutes)
  • Introduction to Azure Cloud Development (2 minutes)
  • Introduction to Testing (5 minutes)
  • Cloud Development with Azure Cloud Shell (3 minutes)
  • Azure Cloud Shell Continuous Integration from Zero (12 minutes)
  • Introduction to GCP Cloud Development (0 minutes)
  • Development Onboarding with GCP (8 minutes)
  • Introduction to Continuous Delivery (4 minutes)
  • Cloud Development with Google Cloud Shell (6 minutes)
  • GCP Google App Engine Continuous Delivery from Zero (9 minutes)
  • Microservices with GCP Cloud Run (4 minutes)
  • Using Google Cloud Functions (6 minutes)
  • Readings:
  • Cloud Onboarding with Amazon Web Services (AWS) (10 minutes)
  • Key Terms (10 minutes)
  • Review GitHub Actions GitHub Project (10 minutes)
  • What is Amazon CodeCatalyst (10 minutes)
  • What is CodeWhisperer? (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • Cloud Onboarding for Azure (10 minutes)
  • What is a Makefile and Why Do You Need it? (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • Cloud Onboarding for GCP (10 minutes)
  • GAE CD GitHub Source Code Walkthrough (10 minutes)
  • Lesson Reflection (10 minutes)
  • Multi-Cloud Continuous Integration (10 minutes)
  • Quizzes:
  • Quiz-Create an AWS Cloud Development Environment (30 minutes)
  • Quiz-Create an Azure Cloud Development Environment (30 minutes)
  • Quiz-Create a GCP Cloud Development Environment (30 minutes)
  • Cloud Onboarding Quiz (30 minutes)
  • Discussion Prompts:
  • Pros and Cons of Cloud-based Development Environment (10 minutes)
  • Strengths and Weaknesses of Testing (10 minutes)
  • Continuous Integration (CI) and Continuous Delivery (CD) (10 minutes)
  • Ungraded Labs:
  • Python Scaffold (60 minutes)
  • Makefile Hello World (60 minutes)
  • Python Flask Hello World (60 minutes)

4. Module 4: Evaluating the Cloud Service Model (6 hours)

  • Videos:
  • Introduction to Cloud Computing (2 minutes) [Preview module]
  • What is Cloud Computing? (3 minutes)
  • Cloud Computing Service Models (4 minutes)
  • Introduction to Building Multiple Websites (2 minutes)
  • Building a Static S3 Website on AWS (5 minutes)
  • Using AWS Lambda Console to Build Python Lambda Function (5 minutes)
  • Building a Serverless Website on AWS Lambda (5 minutes)
  • Building a Website on an EC2 Virtual Machine (10 minutes)
  • Building a Website using PaaS with AWS Beanstalk (12 minutes)
  • Static Websites with Zola (2 minutes)
  • Customizing Zola Theme (3 minutes)
  • Introduction to Cloud Computing Economics (2 minutes)
  • Cloud Computing Economics: A Story (2 minutes)
  • Cloud Economics Deep Dive (8 minutes)
  • Readings:
  • Key Terms (10 minutes)
  • Cloud Computing Service Models (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • AWS Lambda Console Gotchas (10 minutes)
  • Building Multiple Types of Websites (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • Lesson Reflection (10 minutes)
  • Continuous Delivery with AWS Elastic Beanstalk (10 minutes)
  • Quizzes:
  • Quiz-Cloud Computing Service Models (30 minutes)
  • Quiz-Build Multiple Websites: Static, Serverless, Virtualized, PaaS (30 minutes)
  • Quiz-Case Studies of Cloud Computing Economics (30 minutes)
  • Cloud Service Model Quiz (30 minutes)
  • Discussion Prompts:
  • Cloud Service Model (10 minutes)
  • Serverless Web Applications (10 minutes)
  • Economics of Cloud Computing (10 minutes)
  • Ungraded Lab:
  • Zola Static Site (60 minutes)

5. Module 5: Applying DevOps Principles (9 hours)

  • Videos:
  • Introduction to DevOps (1 minute) [Preview module]
  • DevOps in the Real World (1 minute)
  • Benefits of DevOps (3 minutes)
  • DevOps Best Practices (3 minutes)
  • Introduction to Managing Cloud Infrastructure using IaC (1 minute)
  • IaC in the Real World (2 minutes)
  • What is IaC? (2 minutes)
  • Launching a VM with Terraform on GCP (5 minutes)
  • Hello World AWS CDK for Python (7 minutes)
  • Introduction to Continuous Pipelines (1 minute)
  • Continuous Delivery Overview (2 minutes)
  • Continuous Delivery Deep Dive (3 minutes)
  • Continuously Deploy Flask Machine Learning Application with Azure (3 minutes)
  • Continuous Delivery Pipeline with a Lint Operation using Azure (3 minutes)
  • Initial Setup of AWS Cloud9 and GitHub for Hugo (4 minutes)
  • Build Hugo Directory in AWS Cloud9 (20 minutes)
  • Copy Hugo Data into AWS Cloud9 S3 Bucket (4 minutes)
  • Automatic Updating of Hugo in AWS Cloud9 (19 minutes)
  • Readings:
  • Key Terms (10 minutes)
  • What is DevOps? (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • What is Infrastructure as Code (IaC)? (10 minutes)
  • Create a Linux VM with Infrastructure in Azure using Terraform (10 minutes)
  • Lesson Reflection (10 minutes)
  • Key Terms (10 minutes)
  • Continuous Delivery for Hugo Static Site from Zero (10 minutes)
  • Lesson Reflection (10 minutes)
  • Create a Continuous Delivery Pipeline for an AWS Website (10 minutes)
  • Next Steps (10 minutes)
  • Quizzes:
  • Quiz-Develop Continuous Pipelines (30 minutes)
  • DevOps Principles Quiz (30 minutes)
  • Discussion Prompts:
  • DevOps Core Principles (10 minutes)
  • Infrastructure as Code (10 minutes)
  • Continuous Delivery (10 minutes)
  • Ungraded Labs:
  • Explore Hugo Static Website Builder (60 minutes)
  • Sandbox Jupyter (60 minutes)
  • Sandbox VSCode (60 minutes)
  • Sandbox Linux Desktop (60 minutes)
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Dynamic Programming: Applications In Machine Learning and Genomics (edX) EdX
University of California, San Diego,UC San DiegoX

Dynamic Programming: Applications In Machine Learning and Genomics (edX)

Sci: Biology & Life Sciences Sci: Mathematics

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution. If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

Self Paced
Self-Paced
Machine Learning Genomics Dynamic Programming
Details
Computing for Data Analysis (edX) EdX
Georgia Institute of Technology,GTx

Computing for Data Analysis (edX)

CS: Software Engineering Statistics & Data Analysis

A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning. The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data. In the course, you’ll see how computing and mathematics come together.

Aug 24th 2026
13-24 Weeks
Programming Python Computing
Details
Data Science: R Basics (edX) EdX
HarvardX,Harvard University

Data Science: R Basics (edX)

Statistics & Data Analysis Data Science

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

Self Paced
Self-Paced
Probability Machine Learning Inference
Details
Fundamentals of TinyML (edX) EdX
HarvardX,Harvard University

Fundamentals of TinyML (edX)

Statistics & Data Analysis Data Science

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML. What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.

Self Paced
Self-Paced
Machine Learning Embedded Systems Data Science
Details
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Statistics & Data Analysis Data Science

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
Probability Machine Learning Regression
Details
Python for Data Engineering Project (edX) EdX
IBM

Python for Data Engineering Project (edX)

Statistics & Data Analysis

An opportunity to apply your foundational Python skills via a project, using various techniques to collect and work with data. Journey into the realm of becoming a Data Engineer and apply your basic Python knowledge of working with data. You will exercise various techniques in Python to extract data in multiple file formats from different sources, transform it into specific datatypes, and then prepare it for loading it into a database.

Self Paced
Self-Paced
Python APIs Jupyter Notebooks
Details
Applications of TinyML (edX) EdX
HarvardX,Harvard University

Applications of TinyML (edX)

Statistics & Data Analysis

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition. Do you know what happens when you say “OK Google” to a Google device? Is your Google Home always listening? Following on the Foundations of Tiny ML course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.

Self Paced
Self-Paced
Machine Learning Anomaly Detection Tiny Machine Learning (TinyML) Professional Certificate
Details
Fundamentos TIC para profesionales de negocios: Ciberseguridad e Implicaciones sociales (edX) EdX
Universitat Politècnica de València,UPValenciaX

Fundamentos TIC para profesionales de negocios: Ciberseguridad e Implicaciones sociales (edX)

Computer Science

¿Tienes que trabajar con las Tecnologías de la Información y te faltan conocimientos? Profundiza en las implicaciones sociales de las TIC, como la Ciberseguridad, la ergonomía o los puestos de trabajo relacionados. Este curso forma parte de una serie de 5 cursos de introducción al uso de sistemas de información en las empresas que te introducirá en el apasionante mundo de las TIC.

Self Paced
Self-Paced
Cloud Computing Hackers Fundamentos TIC para profesionales de negocios Professional Certificate
Details
IoT Systems and Industrial Applications with Design Thinking (edX) EdX
École Polytechnique Fédérale de Lausanne,EPFLx

IoT Systems and Industrial Applications with Design Thinking (edX)

Engineering Eng: Electronics

The first MOOC to provide a comprehensive introduction to Internet of Things (IoT) including the fundamental business aspects needed to define IoT related products. Internet of Things (IoT) and smart connected devices have radically changed the way our world works and how companies operate and create new businesses.

Self Paced
Self-Paced
Machine Learning Design Thinking IoT
Details