Course Information

  • Sessions 2 days
  • Duration 16 hrs
  • Level Beginner
  • Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Skills Framework

TSC Title
Artificial Intelligence Application in Product Development
TSC Code
ICT-TEM-3034-1.1

Funding Validity

Funding for this course is valid from 23 Sep 2024 to 22 Sep 2026. Register and complete the course within this period to qualify for funding support.

Learning Outcomes

By end of the course, learners should be able to:

  • LO1: Implement AI applications with Amazon Q business and AWS AI models.
  • LO2: Deploy AWS AI Bedrock workflows according to plan and machine learning techniques.
  • LO3: Identify and report issues with AI applications and data and implementation procedures.
  • LO4: Maintain data interoperability during AI development in accordance to principles of data management.
  • LO5: Perform data cleaning techniques to optimize AWS Foundation models.

Course Brochure

Certification

  • Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.
  • OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - AWS Certified AI Practitioner Training

Course Code: TGS-2024049338
  • WSQ
  • SFC
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES
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What's This Course About

This WSQ AWS Certified AI Practitioner Training provides learners with a comprehensive foundation in AI and machine learning using AWS technologies. Participants will explore fundamental AI concepts, machine learning workflows, and generative AI capabilities to develop AI-driven applications. The course covers AWS AI services, including Amazon Q and AWS AI Bedrock, enabling learners to deploy and optimize foundation models effectively. Emphasis is placed on data management, interoperability, and implementing best practices for AI development within an AWS ecosystem.

Through hands-on learning, participants will apply AI security, compliance, and governance frameworks to ensure ethical and responsible AI deployment. The course also covers prompt engineering, model fine-tuning, and performance evaluation to enhance AI system efficiency. By the end of this training, learners will be equipped with the skills to design, implement, and maintain AI applications using AWS, preparing them for AWS AI certification and real-world AI solution deployment.

WSQ Funding

Full Fee $900.00 Before GST
GST $81.00 9% of fee
Baseline Nett $531.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $351.00 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible companies can use their $10,000 SFEC to cover up to 90% of out-of-pocket course fees for classes ending on or before 30 Nov 2026. A redesigned SFEC starts on 1 Dec 2026.

View on SkillsFuture for Business

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used.

Direct Application on SkillsFuture Portal

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year.

Submit UTAP Claim

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

Check PSEA Eligibility

  • Scroll down to “Keyword Tags” to verify for PSEA eligibility.
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form, then submit the completed form to us:

PSEA Withdrawal FormSubmit PSEA Form

Course FeeBefore Funding

$900.00 (GST-exclusive)
$981.00 (GST-inclusive)

Course Date

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 : Fundamentals of AI and ML

Basic AI concepts and terminologies

Practical use cases for AI

Machine Learning development lifecycle

Topic 2: Fundamentals of Generative AI

Basic concepts of generative AI.

Capabilities and limitations of generative AI for solving business problems

AWS infrastructure and technologies for building generative AI applications

Topic 3: Applications of Foundation Models

Design considerations for applications that use foundation models

Effective prompt engineering techniques

Training and fine-tuning process for foundation models

Methods to evaluate foundation model performance

Amazon Q

Topic 4: Guidelines for Responsible AI

Development of AI systems that are responsible.

Importance of transparent and explainable models.

Topic 5: Security, Compliance, and Governance for AI Solutions

Methods to secure AI systems.

Governance and compliance regulations for AI systems.

Assessment

  • Written Exam
  • Practical Exam

Course Info

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
  • 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 Year Group : 21-65 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

  • An AWS account – Free Tier available; a credit card is required to sign up

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.

  1. Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
  2. We email you an invoice showing the course fee after funding.
  3. 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.
  4. Click Submit a Claim and search for this course by its course code, TGS-2024049338.
  5. 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).
  6. 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.
  1. Check your company's SFEC eligibility and balance on EnterpriseSG.
  2. 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.
  3. Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
  4. 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.

  1. Make sure your NTUC membership is paid up before the course starts.
  2. Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
  3. Attend at least 75% of the course and sit all assessments.
  4. 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.
  5. 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).
  6. 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.

  1. Check that this course shows the PSEA tag on its MySkillsFuture page.
  2. Check your PSEA balance on MOE's Student Finance System with Singpass.
  3. Download and complete the PSEA withdrawal form, then submit it to us.
  4. 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.
  5. 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.

  1. 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.
  2. Discuss the decision with the assessor or the assessment manager first.
  3. 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.
  4. Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
  5. 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.
  6. You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

Job Roles

  • AI Practitioner
  • Machine Learning Engineer
  • Data Scientist
  • AI Developer
  • Cloud AI Specialist
  • AI Solutions Architect
  • Data Engineer
  • AI Consultant
  • AI Research Scientist
  • AWS Cloud Engineer
  • Business Intelligence Developer
  • AI Product Manager
  • AI Analyst
  • Machine Learning Operations (MLOps) Engineer
  • Data Analyst
  • Cloud Solutions Architect
  • Software Engineer
  • Technical Program Manager
  • IT Project Manager
  • Systems Architect

Trainers

Trainers

Dr. Alfred Ang holds the very certification this course prepares for: he is an AWS Certified AI Practitioner, as well as an AWS Certified Solutions Architect and Cloud Practitioner. He is also an IBM certified instructor for the AI Practitioners course, a Microsoft Certified AI Engineer Associate (AI-102) and Data Scientist (DP-100), CompTIA Security+ certified, and the holder of a Certificate in AI Ethics and Governance (Senior Professional) from NTU, a combination that lines up closely with the exam's coverage of AI fundamentals, generative AI, responsible AI and security. As a full-stack AI developer and Agentic AI solution architect, he builds cloud-native AI applications using AWS, Kubernetes and DevOps practices, and he serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd and founder of Tertiary Courses. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, with more than 20 peer-reviewed papers and more than 20 US patents as co-inventor. Among the more than 300 public repositories on his GitHub, ai-exams is a full-stack certification practice-exam platform with timed exam and practice modes and AI-assisted question generation, and aiexamapp is its native iOS companion on the App Store. Building those tools has given him a close understanding of how certification candidates prepare and where they tend to struggle.

In WSQ - AWS Certified AI Practitioner Training, Dr. Ang guides learners through all five areas of the exam with a practitioner's perspective. The course begins with the fundamentals of AI and machine learning, covering core terminology, practical use cases and the machine learning development lifecycle. It then moves to the fundamentals of generative AI, its capabilities and limitations for business problems, and the AWS infrastructure for building generative AI applications. The third topic, applications of foundation models, covers design considerations, effective prompt engineering, training and fine-tuning, evaluation of model performance, and AWS services such as Amazon Q and Amazon Bedrock. The final two topics address guidelines for responsible AI, including transparent and explainable models, and security, compliance and governance for AI solutions, where his AI ethics and governance training and Security+ certification add depth to the exam material. Because he designs and deploys AI applications on cloud infrastructure himself, he can explain why AWS services are put together the way they are, which makes scenario questions far easier to reason through than memorised facts. He also shares the exam-preparation insight that comes from holding the certification and building a practice-exam platform. As an ACTA-certified trainer and DACE-certified curriculum developer, he balances concept, demonstration and exam-style review, so learners leave with both practical AWS AI knowledge and the confidence to sit the certification. He also explains the exam format and question styles so learners know exactly what to expect on the day. To see his AI and cloud projects, 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 AI and data analytics specialist with experience in software engineering, cloud computing and applied machine learning. An ACLP-certified trainer, he has delivered AI and analytics programmes under national initiatives such as IMDA's TechSkills Accelerator and SGUnited, and has led AI training for SAP, Temasek Polytechnic and IMDA. His expertise spans Python, TensorFlow and AWS SageMaker, alongside NLP work in sentiment analysis, topic modelling and text classification, and he is known for simplifying complex AI concepts into practical, real-world applications.

In this WSQ AWS Certified AI Practitioner Training, he helps learners build a firm foundation in AI and machine learning on AWS. Participants cover basic AI concepts and terminology, practical use cases and the machine learning development lifecycle, then move on to the capabilities and limitations of generative AI, the AWS infrastructure for building generative AI applications, and design considerations for applications that use foundation models, including effective prompt engineering. His sessions connect these ideas to real business challenges, so learners understand when and how to apply AWS AI services and are well prepared to sit the AWS Certified AI Practitioner exam.

Truman Ng is an IT and AI systems consultant with more than 20 years of experience in cloud computing, data analytics and intelligent automation. He has led digital transformation projects that integrate AI and cloud technologies across manufacturing, logistics and enterprise sectors, giving him a practical view of how AI services are adopted inside real organisations. He holds the AWS Certified Solutions Architect, PMP and Huawei HCIE certifications, and as an ACTA-certified trainer he is recognised for simplifying technical content and delivering structured, outcome-focused training.

In WSQ AWS Certified AI Practitioner Training, Truman gives learners a practical understanding of AI principles and how they are implemented on AWS. He covers the fundamentals of AI and machine learning and the ML development lifecycle, then moves into generative AI, its capabilities and limitations, and the AWS infrastructure for building generative AI applications, including Amazon Q and Amazon Bedrock. Learners explore design considerations for foundation model applications, prompt engineering, fine-tuning and evaluation. His sessions emphasise hands-on use of AWS AI services, preparing participants for the AWS Certified AI Practitioner exam and to contribute confidently to AI projects at work.

Amin Mahetar is a cloud and AI architect whose experience spans cloud infrastructure, cybersecurity and AI-driven transformation. He has held leadership and consulting roles at global organisations such as Cisco, GovTech and Deutsche Bank, implementing large-scale automation and machine learning solutions, and his expertise lies in applying AI and automation to modernise cloud-based systems securely and efficiently. An AWS Certified Solutions Architect, Google Cloud Professional Architect and CISSP, he is also a Microsoft Certified Trainer and Azure Administrator Associate who has managed enterprise-scale cloud deployments in finance, logistics and manufacturing.

In this WSQ course, Amin introduces learners to the fundamentals of AI and machine learning, the machine learning development lifecycle and practical AI use cases, before moving into generative AI, its capabilities and limitations for business problems, and the AWS infrastructure for building generative AI applications. He shows how AWS AI services such as SageMaker, Bedrock and Comprehend support intelligent automation and data-driven decisions, and how foundation models are applied in practice. His sessions emphasise responsible deployment and alignment with organisational goals, giving participants a holistic understanding of AWS AI tools and solid preparation for the AWS Certified AI Practitioner exam.

Mohan Pothula is a data engineering and AI systems specialist whose work spans cloud architecture, automation and data-driven innovation. He has delivered enterprise solutions for organisations such as DBS Bank and SPH Media, focusing on AI integration, DevOps and the optimisation of analytics pipelines. He holds certifications in AWS, Kubernetes and Cloud DevOps, and is well versed in operationalising AI models within cloud environments. His structured teaching style helps learners understand AI technologies and apply them effectively in production contexts.

In this WSQ AWS Certified AI Practitioner training, Mohan guides learners through the core concepts of AI, machine learning and cloud-based automation within the AWS ecosystem. Participants cover the fundamentals of AI and ML, including key terminology, practical use cases and the machine learning development lifecycle, then move on to generative AI, its capabilities and limitations for business problems, and the AWS infrastructure used to build generative AI applications, before exploring applications of foundation models. His sessions cover data preparation, model deployment and AI ethics, blending theory with hands-on application. Learners gain the confidence to use AWS AI services effectively and to support business innovation and intelligent automation initiatives, while preparing for the certification exam.

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