DevOps and Cloud with AI

Week 3

Module 3: Python fundamentals

• Foundations of Python

Week 4

Module 4: Getting started with Linux

• Linux fundamentals in DevOps

Week 5

Module 5: Version control with Git

• Version control

• Git introduction

• Git installation

• Commonly used commands in Git

• Working with a remote repository

Week 6: Maven for building

• Introduction to Maven

• Project configuration and lifecycle

• Dependency management

• Build automation and continuous integration

• Advanced Maven concepts

Week 7: Jenkins for continuous integration and continuous deployment (CI/CD)

• Introduction to continuous integration

• Jenkins master-slave architecture

• Understanding CI/CD pipelines

• Creating an end-to-end automated CI/CD pipeline

Week 8: Continuous automated testing

• Introduction to test pyramid

• Tools and techniques for testing

• Creating test cases in Playwright - UI End-to-end testing

• Test integration with Jenkins

Week 9: Containerisation using Docker

• Introduction to containerisation

• Image management and registry

• Storage in Docker

• Container orchestration using Docker

• Networking and security

• Docker compose and more

Week 10 & 11: Orchestration using Kubernetes

• Introduction to container orchestration

• Kubernetes architecture and core concepts

• Kubernetes services and scheduling

• Kubernetes controllers

• Storage in Kubernetes

• Securing and troubleshooting the cluster

Week 12: Configuration management using Ansible

• Introduction to configuration management

• Server provisioning using Ansible CLI and playbooks

• Ansible modules and roles

• Ansible on cloud

• Automating continuous integration

Week 13: Monitoring using Prometheus and Grafana

• Introduction to Prometheus and Grafana

• Prometheus and Grafana setup

• Monitoring using Prometheus

• Dashboard visualisation using Grafana

• Creating a dashboard to monitor the pipeline

Nagios

• Introduction to Nagios

• Installing Nagios

• Nagios plugins (NRPE) and objects

• Nagios commands and notifications

Week 14: Continuous Monitoring using Elastic (ELK) Stack

• Introduction to continuous monitoring

• What is ELK?

• Components of ELK

• ELK Flow

• Features of ELK

• ELK Installation

Week 15: Foundations of distributed systems

• Introduction to modern application architecture

• DNS fundamentals

• The HTTP/S protocol

• Introduction to REST APIs and JSON

• API authentication patterns

• Scaling with CDNs, caching and load balancers

Week 16: Cloud fundamentals

• Why cloud?

• Introduction to cloud computing

• Cloud service types, cloud service providers, cloud networking and security basics

• Why DevOps on cloud?

Week 17: AWS DevOps

• Introduction to AWS

• AWS services-IAM, EC2, S3 and CloudWatch

• DevOps using AWS and CloudWatch

Weeks 18 & 19: Azure DevOps

• Introduction to Azure

• Azure resource management

• DevOps using Azure

• Azure compute services

• Azure networking and security

• Azure storage

Weeks 20 & 21: Advanced Cloud Computing

• Serverless computing (Lambda and Azure functions)

• Infrastructure as Code

• Cloud security best practices

• Hybrid cloud and multi-cloud strategies

Week 22: Essential DevOps Security – DevSecOps

• Introduction to DevSecOps

• DevSecOps in different stages

• DevSecOps tooling

• Security assessment and posture management

• Advanced DevSecOps

Week 23: Introduction to artificial intelligence and machine learning

• Overview of AI and its subfields

• Supervised, unsupervised and reinforcement learning revisited

• Overview of Python libraries: Sklearn, Tensorflow, PyTorch and Keras

• Real-world applications of AI and machine learning

Week 24: Introduction to ANN

• Basics of Artificial Neuron and activation functions

• Perceptron

• Multi-layer Neural Networks

• Basics on training Neural Networks with Gradient Descent

Week 25: AI in cloud operations and DevOps pipelines

• How AI optimises resource allocation in cloud infrastructure

• AI tools for continuous integration and deployment (CI/CD)

• Automated performance tuning using machine learning

• Enhancing workflow automation in hybrid cloud environments

Week 26: AI for predictive maintenance and monitoring

• Using AI for anomaly detection in server and application logs

• Machine learning models for proactive infrastructure health checks

• Predictive analytics for identifying potential system failures

• AI-powered dashboards for real-time monitoring and alerts

Week 27: AI for DevOps automation

• Automated code review and testing using AI tools

• AI-driven recommendations for infrastructure scaling

• Generative AI for creating CI/CD pipeline configurations

• Intelligent task prioritisation and workload management

Week 28: AI-Driven security in cloud and DevOps

• AI for threat detection and response in cloud environments

• Identifying vulnerabilities using AI-based penetration testing

• Security automation with AI in DevOps workflows

• Using machine learning to analyse and mitigate security risks

Week 29 & 30: Capstone project

• A two-week capstone project during the programme will help you apply concepts learned in a real-world scenario

The programme is designed for professionals minimum diploma or equivalent qualification. IT domain experience preferred.

The curriculum includes topics like Azure DevOps, AI in cloud operations and DevOps pipelines, AI for DevOps Automation, Monitoring using Prometheus and Grafana, Orchestration using Kubernetes, Jenkins for CI/CD and many more.

This 30-week (6-8 hours/week ) programme offers a focused and practical deep learning of DevOps and Cloud with AI programme. It’s perfect for working professionals looking to upskill through a high-impact deep learning certification.

The programme is delivered entirely online, featuring both live sessions from the NUS School of Computing faculty and industry experts. Pre-recorded content supports flexible learning, while live interaction enhances understanding.

Participants will learn through tool-based labs and 15+ tools. Gain practical expertise with Git, Jenkins, Docker, Kubernetes, AWS, Azure and AI/ML frameworks that are trending.

You can expect to invest 6-8 hours per week over 30 weeks. This includes video lectures, assignments, capstone project, simulation-base modules, tool-based learning and live discussions, mirroring the structure of DevOps and Cloud with AI from a top global university.

Yes. You can pay the full fee upfront or choose between two or three instalments. This flexibility supports learners globally. The instalment plans and financial support options are available. Participants can contact the programme advisors for more information.

Yes. Participants will collaborate with global peers during live sessions and group projects. This not only builds connections but also deepens understanding of DevOps, cloud and AI and deep learning concepts, offering a community like that of a structured AI certification programme.

Minimum diploma or equivalent qualification. IT domain experience preferred.

You’ll have access to the learning platform and resources for 12 months after programme completion, valuable for review and keeping pace with the rapidly evolving field of DevOps, cloud and AI.

Taught by world-class faculty from the NUS School of Computing, this programme equips you with the tools, frameworks and strategic insight to lead confidently in an era where DevOps, cloud and AI converge empowering you to stay ahead in the global tech transformation.

We encourage learners to complete the course to fully understand the concepts and derive valuable learning outcomes. However, if you still choose to withdraw, you may request a full refund within 7 days of payment or 14 days after course commencement, whichever comes later. After this period, the course fee becomes non-refundable.