Course Details
Course Details
What You'll Learn
Topic 1: AI-Assisted Cloud Environment Setup and Service Integration
Topic 2: AI-Powered Cloud Deployment, Installation and Database Testing
Topic 3: AI-Assisted Cloud Troubleshooting, Security and Quality Management
Assessment
- Written Exam
- Practical Exam
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- A free account on a generative AI assistant of your choice, e.g. ChatGPT, Google Gemini or Microsoft Copilot (the free tier is sufficient)
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2025056362.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- Cloud Solutions Architect
- Cloud Systems Engineer
- Cloud Infrastructure Specialist
- IT Cloud Support Engineer
- Cloud Project Manager
- Cloud DevOps Engineer
- Cloud Governance Analyst
- Infrastructure and Operations Analyst
- IT Systems Integrator
- Cloud Deployment Consultant
- Cloud Application Developer
- Virtualization Engineer
- Database Administrator (Cloud)
- IT Compliance Officer
- Disaster Recovery Specialist
- Technical Support Engineer (Cloud)
- Platform Engineer
- Network & Cloud Security Analyst
- Systems Administrator
- Cloud Automation Specialist
Trainers
Trainers
Cloud infrastructure is where Dr. Alfred Ang's AI applications actually run, and he manages that stack himself. A full-stack AI developer and Agentic AI solution architect, he is Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, where he designs and deploys end-to-end AI-powered applications on cloud-native platforms across AWS, Kubernetes, DevOps and data engineering, with emphasis on system architecture, security, observability and performance optimisation. He is an AWS Certified Solutions Architect, AI Practitioner and Cloud Practitioner, CompTIA Security+ certified and a Microsoft Certified AI Engineer Associate (AI-102), with a PhD in Electrical Engineering from NUS and ITIL and PMP certifications. His 300+ public GitHub repositories include a training environment management platform that runs Docker-based WordPress, Ubuntu and Kali Linux labs in the browser with role-based access, on-demand container start and stop and idle auto-shutdown; a franchise course registration platform deployed from a single codebase to separate partner sites with Docker and Coolify; a self-hosted IoT platform; native iOS apps with CI/CD pipelines that auto-submit builds on every push; and a collection of Claude Code skills that include pushing images to Docker Hub and scanning code for secrets before deployment. He has also worked in the defence, electronics and semiconductor industries, and as an ACTA-certified trainer and DACE-certified curriculum developer he designs Skills Framework-aligned WSQ courses in cloud computing, cybersecurity and modern application development. His teaching draws directly on the systems he operates rather than on theory alone.
In this WSQ course, Dr. Ang shows participants how AI tools can support every stage of planning, implementing, monitoring and maintaining cloud environments. Learners begin with AI-assisted environment setup and service integration, interpreting business and technical requirements, comparing service and deployment models and using AI to recommend suitable architectures covering virtualisation, networking, storage and databases. They then use AI to generate configuration guidance, automate routine tasks, deploy applications, install components and test databases. The final topic covers troubleshooting, security and quality management, including governance, access control and cost management, with AI helping to diagnose faults and produce technical documentation. Because he runs production workloads himself, he is clear that AI-generated configurations must be reviewed, tested and secured before they reach production, and he shares practical lessons from his own deployments about permissions, monitoring and cost control. His style is hands-on and lab-based, so learners practise in real cloud consoles and leave able to use AI to work faster and more reliably on their organisation's cloud estate. He also shows how to write effective prompts for infrastructure work, giving the AI the right context about the environment, constraints and security requirements, and how to verify its suggestions against official documentation. Learners finish with documentation and runbooks produced during the labs, which they can adapt for their own organisations. To see the cloud-deployed platforms and DevOps tooling he has built, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Quah Chee Yong is an ACLP-certified trainer and data science professional with extensive experience in Python, natural language processing (NLP) and machine learning. He has led AI training programmes for SAP, Temasek Polytechnic and IMDA under the SGUnited Mid-Career Pathways initiative. He is also a data analytics and automation specialist with experience in database systems, cloud integration and business intelligence development. His expertise includes Microsoft Power Platform, Python and SQL, allowing him to bridge data processing with workflow automation, and he has worked on numerous digital transformation initiatives that combine analytical thinking with practical implementation.
In WSQ AI for Cloud Computing, he draws on his cloud integration and database experience to show how AI can help plan, implement and maintain cloud environments. Learners use AI to set up cloud environments and integrate services, then apply AI-powered approaches to cloud deployment, installation and database testing. The final topic covers AI-assisted troubleshooting, security and quality management. True to his interactive style, he guides participants through case-based exercises that simulate real-world challenges, so they gain confidence in using AI tools to manage cloud infrastructure, resolve issues and apply sound governance in their own organisations.
Amin Mahetar is a cloud and DevOps engineer whose expertise spans IT infrastructure, infrastructure automation and cloud solutions across AWS and Microsoft Azure. A Microsoft Certified Trainer and Azure Administrator Associate, he has implemented and managed enterprise-scale cloud deployments in finance, logistics and manufacturing, and he brings practical knowledge of system architecture, automation scripting with PowerShell and Azure CLI, and CI/CD integration. His areas of expertise also include virtualisation, networking and identity management, and he is experienced in designing and deploying scalable, secure and resilient cloud-based systems.
In this WSQ course, Amin applies that cloud and automation background to using artificial intelligence across the cloud lifecycle. Learners use AI to assist with setting up cloud environments and integrating services, then with deployment, installation and database testing, and finally with troubleshooting, security and quality management. His grounding in virtualisation, networking and identity management helps participants evaluate AI suggestions against how cloud systems actually behave. Known for simplifying complex technical concepts through real-world scenarios, he encourages a problem-solving mindset, so learners leave with the confidence to plan, implement, monitor and maintain cloud environments with AI support.
Nicholas Lu is an IT infrastructure and systems professional with rich experience in Linux administration, automation and containerisation technologies. His expertise covers DevOps tools, Kubernetes orchestration and infrastructure-as-code practices, giving learners a strong technical foundation in modern cloud-native development. His background in system deployment and automation allows him to provide comprehensive insights into managing distributed systems effectively, and in the classroom he adopts a structured, example-driven approach that translates technical depth into accessible, actionable learning outcomes.
In AI for Cloud Computing, Nicholas brings this infrastructure and automation experience to the use of AI tools across the cloud lifecycle. Learners use AI to set up cloud environments and integrate services, interpreting business and technical requirements across service and deployment models, virtualisation, networking and storage. They then apply AI to cloud deployment, installation and database testing, before using it for troubleshooting, security and quality management. His grounding in container-based deployment and infrastructure automation helps participants connect AI recommendations to how cloud systems are actually built and run, so they leave able to plan, implement, monitor and maintain cloud environments with greater confidence in enterprise settings.
Mohan Pothula is an IT consultant and trainer specialising in network engineering, cybersecurity and cloud systems. He has designed and managed secure enterprise infrastructure and supported numerous organisations in optimising their IT operations and strengthening network resilience. His technical expertise spans system administration, virtualisation and network automation, giving him a practical understanding of how cloud environments are built and kept running. As an educator, he brings clarity and structure to his training, ensuring participants grasp both the theory and its application, with a commitment to industry-aligned instruction and practical demonstrations.
In WSQ - AI for Cloud Computing, Mohan draws on his cloud systems and automation background to show how AI can support each stage of cloud operations. Learners use AI to assist with cloud environment setup and service integration, then with cloud deployment, installation and database testing, and finally with cloud troubleshooting, security and quality management. Along the way they explore service and deployment models, virtualisation, networking, storage, databases and application deployment. Participants gain not only certification-ready knowledge but the competence to plan, implement, monitor and maintain cloud environments with AI support in real-world IT settings.
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