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Cloud Software Engineering in the Era of Cloud Computing
Cloud software engineering course that turns “I code” into “I lead” with hands-on, high-impact learning.
- Practice in 14 Hands-On Labs — nothing to install
- 14 Interactive Lessons and 92 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
14 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
14Interactive Lessons
92Topics
14LiveLab
4Videos
59Flashcards
59Glossary of terms
01 / Skills you'll get
What you will be able to do
Enroll in our Cloud Software Development course and master the skills to build, deploy, and secure modern cloud applications…hands-on, from day one.
In this course, dive into requirements engineering for cloud computing, migrating monoliths to microservices, and machine learning in the cloud. Learn how top professionals tackle real-world challenges from security risks to green software testing using Azure and other cutting-edge tools.
You’ll get lab-driven practice with industry-relevant projects, so you can stop just reading about cloud development and start doing it.
- Cloud-Native Requirements Engineering: Master frameworks like REF-SCC to gather and analyze requirements for cloud applications.
- Microservices Migration Strategy: Learn to transition from monolithic architectures to scalable cloud-based microservices.
- Secure Cloud Software Design: Identify and mitigate security risks in cloud-based systems through systematic review and best practices.
- ML-Powered Cloud Development: Apply machine learning techniques (like sentiment analysis and defect prediction) using Azure and cloud platforms.
- Cloud Testing & DevOps Optimization: Implement green software testing, virtualization, and CI/CD pipelines for efficient cloud deployments.
- Big Data Engineering in the Cloud: Process and analyze large-scale datasets using cloud-native tools and distributed systems.
Course Highlights
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14 Structured Lessons Comprehensive coverage of core course objectives
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14 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
14 Interactive Lessons · 92 topics01 Introduction 2 topics +
- Objectives
- Organization
02 Requirements Engineering Framework for Service and Cloud Computing (REF-SCC) 8 topics · 2 LiveLab +
- Introduction
- BPMN as Requirements Engineering Method
- BPMN Requirements Engineering Life Cycle for Service and Cloud Computing (BPMN-RELC-SCC)
- BPMN Combined Infrastructure Overview
- Requirements Engineering Framework for Service and Cloud Computing (REF-SCC)
- Reference Architecture for Service and Cloud Computing
- Experimental Validation
- Conclusion
2 LiveLab in this lesson — see the labs panel →
03 Toward an Effective Requirement Engineering Approach for Cloud Applications 8 topics · 1 LiveLab +
- Introduction
- Related Work
- Cloud Application Evolution
- Key Drivers of Cloud Applications
- Cloud Applications Requirements Engineering
- Cloud Application Qualities and Requirements
- Enabling Technologies for SaaS Qualities
- Conclusion
1 LiveLab in this lesson — see the labs panel →
04 Requirements Engineering for Large-Scale Big Data Applications 5 topics · 2 LiveLab +
- Introduction
- Research Methodology Using Systematic Literature Review
- Related Work
- Requirements Engineering for Big Data
- Conclusion and Future Work
2 LiveLab in this lesson — see the labs panel →
05 Migrating from Monoliths to Cloud-Based Microservices: A Banking Industry Example 6 topics · 1 LiveLab +
- Introduction
- Monolithic Applications: Background and Challenges
- Microservices: A Cloud-Based Alternative
- Building Cloud-Based Applications
- Transitioning from Monoliths to Cloud-Based Microservices
- Conclusion
1 LiveLab in this lesson — see the labs panel →
06 Cloud-Enabled Domain-Based Software Development 6 topics +
- Introduction
- Background
- Motivation and Related Work
- Suggested Development Paradigm
- Discussion
- Conclusion
07 Security Challenges in Software Engineering for the Cloud: A Systematic Review 6 topics +
- Introduction
- Motivation
- Related Works
- Methodology
- Results
- Conclusion and Future Work
08 Software Engineering Framework for Defect Management Using Machine Learning on Azure 13 topics · 1 LiveLab +
- Introduction
- Machine Learning Application to Software Engineering Analytics: Literature Review
- Machine/Deep Learning Approaches to Software Engineering
- Software Engineering Analytics Using Big Data
- Software Defects
- Software Defect Detection Techniques and Tools
- Bug Prediction in Software Development
- Neural Network Approach for Bug Prediction to Estimate Software Costs and to Feed New Requirements
- Service-Oriented Approach to Providing Bug Prediction
- Cloud Software Engineering for Machine Learning Applications
- Experiment with Microsoft Azure Machine Learning
- Evaluating Neural Network Approaches in Software Engineering Analytics
- Conclusion and Future Work
1 LiveLab in this lesson — see the labs panel →
09 Sentiment Analysis of Twitter Data Through Machine Learning Techniques 5 topics · 3 LiveLab +
- Introduction
- Literature Review
- Methodology
- Results
- Conclusions and Future Research
3 LiveLab in this lesson — see the labs panel →
10 Connection Handler: A Design Pattern for Recovery from Connection Crashes 7 topics +
- Introduction
- Related Work
- General Design of a Connection-Oriented Application
- Connection Handler Design Pattern
- Design of Reliable Applications Using the Connection Handler Design Pattern
- Experimental Evaluation
- Conclusion
11 A Modern Perspective on Cloud Testing Ecosystems 5 topics · 2 LiveLab +
- Introduction
- Cloud Testing
- Cloud Testing and Deployment Models
- Tools and Frameworks for Cloud Testing
- Conclusion
2 LiveLab in this lesson — see the labs panel →
12 Green Software Testing in Agile and DevOps with Cloud Virtualization for Environmental Protection 7 topics · 2 LiveLab +
- Introduction
- Cloud Computing and Services on the Cloud
- Green Computing
- Green Software Testing on the Cloud
- Cloud Vendors’ Provision of TaaS
- Green Testing on the Cloud: Agile and DevOps Software Development
- Conclusion
2 LiveLab in this lesson — see the labs panel →
13 Machine Learning as a Service for Software Process Improvement 9 topics +
- Introduction
- Overview of Software Process Improvement
- Measurable Metrics for SPI
- Overview of Machine Learning
- Qualitative Research
- Development of the Maturity Model
- Prototype Development
- Evaluation
- Conclusion and Further Research
14 Comparison of Data Mining Techniques in the Cloud for Software Engineering 5 topics · 1 LiveLab +
- Introduction
- Related Works
- Materials and Methods
- Experimental Studies
- Conclusion
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
14 LiveLabs- Classifying the BPMN Tasks
- Using BPMN as a Requirements Engineering Method
- Key Drivers of Cloud Applications
- Big Data Requirements Engineering
- Classifying the Functional Service Components
- Migrating from Monolith to Microservices
- Integration of AI with Software Engineering
- Performing Sentiment Analysis
- Evaluating the Classifiers Using the Confusion Matrix
- Evaluating a Classification Model Using the Confusion Matrix
- Cloud Testing
- Classifying Testing Model Benefits
- Green Software Testing
- Classifying the CI/CD Phases
- Cloud-Based Data Mining
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
Is cloud computing software engineering?+
They’re cousins. Cloud computing uses software engineering principles but focuses on scalable, distributed systems. This course covers both.
What is the difference between a Cloud or a software engineer?+
Love building infrastructure? Focus on cloud computing. Prefer pure coding? Stick with software engineering.
Both pay well, but cloud roles are exploding right now.
Is cloud a good career? +
Absolutely. Cloud computing skills are in crazy demand right now, and companies are paying top dollar for people who know their way around AWS, Azure, or GCP.
How to become a cloud developer?+
Start with coding basics (Python’s a safe bet), get comfy with a major cloud engineering platform, and dive into real projects. Hands-on labs like the ones in this course are your best friends.
What does a cloud engineer need?+
To become a certified cloud engineer, you'll need cloud platform skills (AWS/Azure/GCP), infrastructure-as-code know-how (like Terraform), security chops, and problem-solving for big, scalable systems.
What is the average Cloud engineer salary?+
Expect to earn an average annual salary of $ 110,000-$160,000 in the beginning. Get good, and you’ll clear $200K, especially with the right certifications.
Keep going
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