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

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
Data Science and Machine Learning Capstone Project (edX) EdX
IBM

Data Science and Machine Learning Capstone Project (edX)

Statistics & Data Analysis Data Science

Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model. Now that you've taken several courses on data science and machine learning, it’s time to put your learning to work on a data problem involving a real life scenario. Employers really care about how well you can apply your knowledge and skills to solve real world problems, and the work you do in this capstone project will make you stand out in the job market.

Self Paced
Self-Paced
Python Machine Learning Data Science
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
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
Network Defense Essentials (NDE) (edX) EdX
EC-Council

Network Defense Essentials (NDE) (edX)

Security & Networking

Network Defense Essentials (NDE) is a first-of-its-kind MOOC certification that provides foundational knowledge and skills in network security with add-on labs for hands-on experience. Network security plays a vital role in most organizations. It is the process of preventing and detecting unauthorized use of an organization’s networking infrastructure.

Self Paced
Self-Paced
Cryptography Cloud Computing Information Security
Details
High-Dimensional Data Analysis (edX) EdX
HarvardX,Harvard University

High-Dimensional Data Analysis (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data. If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis.

Self Paced
Self-Paced
Machine Learning Clustering Data Analysis
Details
Advanced Bayesian Statistics Using R (edX) EdX
University of Canterbury,UCx

Advanced Bayesian Statistics Using R (edX)

Statistics & Data Analysis Data Science

Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods. Advanced Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.

Self Paced
Self-Paced
Calculus Algorithms Machine Learning
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
Machine Learning at the Edge on Arm: A Practical Introduction (edX) EdX
Arm Education,ArmEducationX

Machine Learning at the Edge on Arm: A Practical Introduction (edX)

Engineering Eng: Electronics

This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers. The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

Self Paced
Self-Paced
ML Artificial Intelligence Machine Learning
Details