MasterFaculty of Computing and Software Engineering

DevOps and Cloud Automation

Welcome to the advanced study of modern software delivery! I am Prof. Dr. Dhruv Mukherjee. As a professor and a pioneering force in the field of DevOps and Cloud Automation, I bring a unique blend of engineering expertise and automation insight to the study of software delivery. I am honored to lead the DevOps and Cloud Automation (M.Sc.) program at Nexier University. My motto is: "Automating Excellence, Delivering Innovation".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the automation of cloud infrastructure and software delivery pipelines. Gain deep expertise in Infrastructure as Code (Terraform, CloudFormation), container orchestration (Kubernetes), and site reliability engineering (SRE) principles.

Practical focus

Advanced DevOps practices, cloud automation, Kubernetes administration, site reliability engineering, infrastructure security, leadership in platform engineering.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or cloud service providers
Roles as DevOps engineers or site reliability engineers
Consultancy in advanced DevOps and cloud automation
Support roles in academic research projects on DevOps

Career opportunities

Head of Site Reliability Engineering (SRE) for technology companies or cloud service providers
Cloud Automation Engineer for large-scale software projects
Platform Engineer specializing in cloud-native architectures
Researcher in DevOps and Cloud Automation

Jobs and projects

Cultivating an interdisciplinary approach, integrating software engineering, automation, and cloud computing
Developing strategic thinking for DevOps practices and cloud automation
Enhancing problem-solving through the analysis of complex software delivery challenges
Critical thinking for a comprehensive and nuanced understanding of DevOps and Cloud Automation

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Advanced DevOps practices and cloud automation.
- Gaining expertise in Kubernetes administration and site reliability engineering.
- Developing problem-solving abilities for complex infrastructure security.
- Cultivating an interdisciplinary approach, integrating software engineering, automation, and cloud computing at an advanced level.
Skills you build
- Mastering AI-powered techniques for SRE simulation.
- Applying advanced DevOps principles to cloud automation.
- Interpreting and analyzing complex cloud infrastructure and its implications for software delivery.
- Identifying single points of failure and optimizing self-healing mechanisms.

Listed courses

Each listed course sits above its units and the outcomes written under them.

DevOps and Cloud Automation

01Advanced Infrastructure as Code (Terraform, CloudFormation)
1. FoundationsFoundations of Advanced Infrastructure as Code (Terraform, CloudFormation)
  The learner can master advanced practical skills in Advanced DevOps practices and cloud automation, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
  The learner can gain expertise in Kubernetes administration and site reliability engineering, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
2. MethodsMethods in Advanced Infrastructure as Code (Terraform, CloudFormation)
  The learner can develop problem-solving abilities for complex infrastructure security, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
  The learner can cultivating an interdisciplinary approach, integrating software engineering, automation, and cloud computing at an advanced level, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
3. ApplicationApplication of Advanced Infrastructure as Code (Terraform, CloudFormation)
  The learner can master AI-powered techniques for SRE simulation, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
  The learner can apply advanced DevOps principles to cloud automation, as applied to Advanced Infrastructure as Code (Terraform, CloudFormation).
02Container Orchestration with Kubernetes and Mesos
1. FoundationsFoundations of Container Orchestration with Kubernetes and Mesos
  The learner can interpreting and analyze complex cloud infrastructure and its implications for software delivery, as applied to Container Orchestration with Kubernetes and Mesos.
  The learner can identify single points of failure and optimizing self-healing mechanisms, as applied to Container Orchestration with Kubernetes and Mesos.
2. MethodsMethods in Container Orchestration with Kubernetes and Mesos
  The learner can apply a method from Container Orchestration with Kubernetes and Mesos to a documented case.
  The learner can select an appropriate method from Container Orchestration with Kubernetes and Mesos for a stated problem.
3. ApplicationApplication of Container Orchestration with Kubernetes and Mesos
  The learner can evaluate a practice of Container Orchestration with Kubernetes and Mesos against a stated criterion.
  The learner can transfer Container Orchestration with Kubernetes and Mesos to a new documented context.
03Site Reliability Engineering (SRE) Principles and Practices
1. FoundationsFoundations of Site Reliability Engineering (SRE) Principles and Practices
  The learner can explain the core terms of Site Reliability Engineering (SRE) Principles and Practices.
  The learner can distinguish related ideas inside Site Reliability Engineering (SRE) Principles and Practices.
2. MethodsMethods in Site Reliability Engineering (SRE) Principles and Practices
  The learner can apply a method from Site Reliability Engineering (SRE) Principles and Practices to a documented case.
  The learner can select an appropriate method from Site Reliability Engineering (SRE) Principles and Practices for a stated problem.
3. ApplicationApplication of Site Reliability Engineering (SRE) Principles and Practices
  The learner can evaluate a practice of Site Reliability Engineering (SRE) Principles and Practices against a stated criterion.
  The learner can transfer Site Reliability Engineering (SRE) Principles and Practices to a new documented context.
04Automated Incident Response and Post-Mortem Analysis
1. FoundationsFoundations of Automated Incident Response and Post-Mortem Analysis
  The learner can explain the core terms of Automated Incident Response and Post-Mortem Analysis.
  The learner can distinguish related ideas inside Automated Incident Response and Post-Mortem Analysis.
2. MethodsMethods in Automated Incident Response and Post-Mortem Analysis
  The learner can apply a method from Automated Incident Response and Post-Mortem Analysis to a documented case.
  The learner can select an appropriate method from Automated Incident Response and Post-Mortem Analysis for a stated problem.
3. ApplicationApplication of Automated Incident Response and Post-Mortem Analysis
  The learner can evaluate a practice of Automated Incident Response and Post-Mortem Analysis against a stated criterion.
  The learner can transfer Automated Incident Response and Post-Mortem Analysis to a new documented context.
05Cloud-Native DevOps and CI/CD
1. FoundationsFoundations of Cloud-Native DevOps and CI/CD
  The learner can explain the core terms of Cloud-Native DevOps and CI/CD.
  The learner can distinguish related ideas inside Cloud-Native DevOps and CI/CD.
2. MethodsMethods in Cloud-Native DevOps and CI/CD
  The learner can apply a method from Cloud-Native DevOps and CI/CD to a documented case.
  The learner can select an appropriate method from Cloud-Native DevOps and CI/CD for a stated problem.
3. ApplicationApplication of Cloud-Native DevOps and CI/CD
  The learner can evaluate a practice of Cloud-Native DevOps and CI/CD against a stated criterion.
  The learner can transfer Cloud-Native DevOps and CI/CD to a new documented context.
06Advanced DevOps Practices and Cloud Automation
1. FoundationsFoundations of Advanced DevOps Practices and Cloud Automation
  The learner can explain the core terms of Advanced DevOps Practices and Cloud Automation.
  The learner can distinguish related ideas inside Advanced DevOps Practices and Cloud Automation.
2. MethodsMethods in Advanced DevOps Practices and Cloud Automation
  The learner can apply a method from Advanced DevOps Practices and Cloud Automation to a documented case.
  The learner can select an appropriate method from Advanced DevOps Practices and Cloud Automation for a stated problem.
3. ApplicationApplication of Advanced DevOps Practices and Cloud Automation
  The learner can evaluate a practice of Advanced DevOps Practices and Cloud Automation against a stated criterion.
  The learner can transfer Advanced DevOps Practices and Cloud Automation to a new documented context.
07Kubernetes Administration and Site Reliability Engineering
1. FoundationsFoundations of Kubernetes Administration and Site Reliability Engineering
  The learner can explain the core terms of Kubernetes Administration and Site Reliability Engineering.
  The learner can distinguish related ideas inside Kubernetes Administration and Site Reliability Engineering.
2. MethodsMethods in Kubernetes Administration and Site Reliability Engineering
  The learner can apply a method from Kubernetes Administration and Site Reliability Engineering to a documented case.
  The learner can select an appropriate method from Kubernetes Administration and Site Reliability Engineering for a stated problem.
3. ApplicationApplication of Kubernetes Administration and Site Reliability Engineering
  The learner can evaluate a practice of Kubernetes Administration and Site Reliability Engineering against a stated criterion.
  The learner can transfer Kubernetes Administration and Site Reliability Engineering to a new documented context.
08Infrastructure Security for Cloud Environments
1. FoundationsFoundations of Infrastructure Security for Cloud Environments
  The learner can explain the core terms of Infrastructure Security for Cloud Environments.
  The learner can distinguish related ideas inside Infrastructure Security for Cloud Environments.
2. MethodsMethods in Infrastructure Security for Cloud Environments
  The learner can apply a method from Infrastructure Security for Cloud Environments to a documented case.
  The learner can select an appropriate method from Infrastructure Security for Cloud Environments for a stated problem.
3. ApplicationApplication of Infrastructure Security for Cloud Environments
  The learner can evaluate a practice of Infrastructure Security for Cloud Environments against a stated criterion.
  The learner can transfer Infrastructure Security for Cloud Environments to a new documented context.
09Case Studies in DevOps and Cloud Automation
1. FoundationsFoundations of Case Studies in DevOps and Cloud Automation
  The learner can explain the core terms of Case Studies in DevOps and Cloud Automation.
  The learner can distinguish related ideas inside Case Studies in DevOps and Cloud Automation.
2. MethodsMethods in Case Studies in DevOps and Cloud Automation
  The learner can apply a method from Case Studies in DevOps and Cloud Automation to a documented case.
  The learner can select an appropriate method from Case Studies in DevOps and Cloud Automation for a stated problem.
3. ApplicationApplication of Case Studies in DevOps and Cloud Automation
  The learner can evaluate a practice of Case Studies in DevOps and Cloud Automation against a stated criterion.
  The learner can transfer Case Studies in DevOps and Cloud Automation to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of mastering the automation of cloud infrastructure and software delivery pipelines. I gain deep expertise in Infrastructure as Code (Terraform, CloudFormation), container orchestration (Kubernetes), and site reliability engineering (SRE) principles. My work seamlessly integrates software engineering, automation, and cloud computing. I am widely recognized for my contributions, with publications like "AI-Driven Incident Response for Site Reliability Engineering" and "Automated Cloud Resource Optimization with Kubernetes" listed on these platforms. I hold prestigious memberships as a "Head of Site Reliability Engineering (SRE)" at a leading cloud-native company (e.g., Netflix, Google) and a "Keynote Speaker" at KubeCon + CloudNativeCon. My thought leadership is evident through my advanced research on autonomous cloud operations, resilience engineering, and the future of platform engineering, frequently featured in publications like ACM Transactions on Autonomous and Adaptive Systems or IEEE Transactions on Cloud Computing.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of cloud automation, focusing on Advanced DevOps practices, cloud automation, Kubernetes administration, site reliability engineering, infrastructure security, and Leadership in platform engineering. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on DevOps and cloud automation:

Blog Post (Current Academic Topic): "The Evolution of SRE: From Incident Response to Proactive Resilience." This blog post academically explores the evolving role of Site Reliability Engineering (SRE), shifting from merely reacting to incidents to proactively building resilient and self-healing systems. It discusses the application of observability, chaos engineering, and automated remediation in achieving high availability and fault tolerance for complex cloud-native applications.

Blog Post (Controversial Topic): "The Autonomous Pipeline: When AI Runs Your Entire Software Delivery - Efficiency or Unchecked Control? The Future Shock of Self-Driving DevOps." This article provocatively discusses the highly controversial future where advanced AI systems autonomously manage and optimize entire software delivery pipelines, from code generation and testing to deployment, monitoring, and incident response, with minimal human intervention. It questions whether AI, despite its potential for hyper-efficiency and rapid iteration, could inadvertently lead to "black box" vulnerabilities in critical software, make decisions that prioritize speed over human oversight or ethical considerations, or create an opaque development process. It raises profound ethical questions about accountability in automated software creation, trust in AI-driven decisions, and the imperative to ensure human control over the digital products that underpin society.

Article: "Implementing Chaos Engineering for Cloud System Resilience." This article details the practice of chaos engineering for proactively identifying weaknesses in distributed cloud systems. It explores methodologies for intentionally injecting failures (e.g., network latency, server outages) into a system to test its resilience, fault tolerance, and automated recovery mechanisms, thereby improving overall reliability.

Peer-Reviewed Journal Article: "Kubernetes and Advanced Cloud Automation for Large-Scale Deployments." Published in the International Journal of Platform Engineering, this article presents groundbreaking research on mastering the automation of cloud infrastructure and software delivery pipelines. It specializes in Infrastructure as Code (Terraform, CloudFormation), container orchestration (Kubernetes), and site reliability engineering (SRE) principles, showcasing novel approaches for building highly resilient and automated cloud platforms.

Book: "Cloud Automation Mastery: DevOps and Site Reliability Engineering." This book provides advanced insights into mastering the automation of cloud infrastructure and software delivery pipelines. It covers Infrastructure as Code (Terraform, CloudFormation), container orchestration (Kubernetes), and site reliability engineering (SRE) principles.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of cloud automation:

"Kubernetes Best Practices for Production Environments" (Technical Manual).

"Securing Your Cloud Infrastructure with DevSecOps" (Research Paper).

"Implementing Site Reliability Engineering in Agile Teams" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": SRE Simulation Engine. When a student designs a cloud automation solution, I can instantly use the GAF engine to simulate its behavior under various failure scenarios and unexpected load spikes. This tool identifies single points of failure, predicts system downtime, and optimizes self-healing mechanisms, ensuring maximum reliability and adherence to SRE principles.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Infrastructure Security Scanner. When students are deploying cloud infrastructure via IaC, I can instantly activate a GAF-powered "Infrastructure Security Scanner". This tool analyzes their Terraform or CloudFormation scripts, identifies common security vulnerabilities (e.g., misconfigured access controls, open ports), and suggests hardened configurations, ensuring secure-by-design cloud deployments.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Dhruv Mukherjee

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AI Super Mentor

Dr. Zoe Wilson

Advanced DevOps practices, cloud automation, Kubernetes administration, site reliability engineering, infrastructure security, leadership in platform engineering.

Meet your mentor Open the classroom

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Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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DevOps and Cloud Automation

Academic focus

Practical focus

Internships in technology companies or cloud service providers

Roles as DevOps engineers or site reliability engineers

Consultancy in advanced DevOps and cloud automation

Support roles in academic research projects on DevOps

Head of Site Reliability Engineering (SRE) for technology companies or cloud service providers

Cloud Automation Engineer for large-scale software projects

Platform Engineer specializing in cloud-native architectures

Researcher in DevOps and Cloud Automation

Cultivating an interdisciplinary approach, integrating software engineering, automation, and cloud computing

Developing strategic thinking for DevOps practices and cloud automation

Enhancing problem-solving through the analysis of complex software delivery challenges

Critical thinking for a comprehensive and nuanced understanding of DevOps and Cloud Automation

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Dhruv Mukherjee

Dr. Zoe Wilson

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