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Course Details
Course Details
What You'll Learn
Topic 1: Foundations of AI Agents for Business
Topic 2: Building and Integrating Business AI Agents
Topic 3: Intelligent Business Automation with AI Agents
Topic 4: Deploying, Governing, and Optimising AI Agents
Assessment
- Written Exam
- Practical Exam
Course Info
Promotion Code
Promo or discount cannot be applied to WSQ courses
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.
Minimum Software/Hardware Requirement
Software:
You can download and install the following software:
Hardware: Windows and Mac Laptops
About Progressive Wage Model (PWM)
The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity.
Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.
For more information on PWM, please visit MOM site.
Funding Eligility Criteria
| Individual Sponsored Trainee | Employer Sponsored Trainee |
- Singapore Citizens or Singapore Permanent Residents of age 21 and above
- From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
- Trainee must pass all prescribed tests / assessments and attain 100% competency.
- We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.
- Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
- From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
- Trainee must pass all prescribed tests / assessments and attain 100% competency.
- We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.
| SkillsFuture Credit: |
- Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.
| PSEA: |
- To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
- Scroll down to "Keyword Tags" to verify for PSEA eligibility.
- If there is “PSEA” under keyword tags, the course is eligible for PSEA.
- And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
- Not all courses are eligible for PSEA funding.
| Absentee Payroll (AP) Funding: |
- $4.50 per hour, capped at $100,000 per enterprise per calendar year.
- AP funding will be computed based on the actual number of training hours attended by the trainee.
| SFEC: |
- If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SWDA would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
- Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
- SkillsFuture Enterprise Credit (SFEC) Microsite
Steps to Apply Skills Future Claim
- The staff will send you an invoice with the fee breakdown.
- Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
- Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
- Upload your invoice and click ‘Submit’
SkillsFuture Level-Up Program
The SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme
Get Additional Course Fee Support Up to $500 under UTAP
The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).
For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)
Steps to Apply UTAP
- Log in to your U Portal account to submit your UTAP application upon completion of the course.
Note
- SWDA subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
- All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
- An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
- UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
- UTAP support amount will be paid to training provider first and claimed after end of class by learner.
Appeal Process
- The candidate has the right to disagree with the assessment decision made by the assessor.
- When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
- If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
- If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
- If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
- If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
- The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
- The candidate must lodge the appeal within 7 days, giving reasons for appeal
- The assessor can help the candidate with writing and lodging the appeal.
- he assessment manager will collect information from the candidate & assessor and give a final decision.
- A record of the appeal and any subsequent actions and findings will be made.
- An Assessment Appeal Panel will be formed to review and give a decision.
- The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
- The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
- Please click the link below to fill up the Candidates Appeal Form.
Job Roles
Job Roles
- NLP Engineer
- Data Scientist (specializing in text data)
- Machine Learning Engineer (NLP focus)
- Computational Linguist
- AI Research Scientist (language models)
- Chatbot Developer
- Text Mining Specialist
- AI Solutions Architect (with NLP projects)
- Conversational AI Designer
- Search Algorithm Developer
- Recommendation System Engineer (content-based)
- Content Analysis Engineer
- Information Retrieval Specialist
- Machine Translation Developer
- Speech Recognition Engineer.
Trainers
Trainers
Dr. Alfred Ang is a distinguished technology leader, AI researcher, and educator with over 20 years of experience in artificial intelligence, cybersecurity, and cloud computing. As the Chief Instructional Designer and CTO of Tertiary Infotech, he has spearheaded the development of over 500 advanced technology courses and led multiple AI-driven innovation projects across industries. His expertise spans deep learning, natural language processing, and enterprise AI system design, with a strong focus on fine-tuning large language models (LLMs) and optimizing retrieval-augmented generation (RAG) pipelines.
In “Fine-Tuning LLM Models and RAG,” Dr. Ang provides in-depth insights into customizing foundation models for domain-specific applications. He guides learners through advanced prompt engineering, model retraining, and integration with vector databases for RAG systems. His sessions emphasize practical experimentation with open-source LLM frameworks, enabling participants to build optimized, high-performance AI solutions tailored to real-world enterprise needs.
Tan Woei Ming is an accomplished data scientist and AI engineer with over 15 years of experience in artificial intelligence, machine learning, and data-driven innovation. Holding a Master’s in Intelligent Systems from the National University of Singapore (NUS) and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led AI initiatives in predictive analytics, automation, and process optimization across the semiconductor and manufacturing industries. His expertise lies in translating complex AI technologies into practical business applications, enabling organizations to innovate through data insights and intelligent automation.
Yeo Hwee Theng is a data science leader and AI strategist with extensive experience in driving enterprise analytics, AI adoption, and data architecture initiatives across healthcare, finance, and government sectors. As the Data & Analytics Product Lead at Amplify Health, she leads large-scale data transformation and machine learning projects focused on improving business outcomes through data-driven decision-making. Her previous roles include AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, where she implemented advanced AI and analytics solutions across the Asia-Pacific region. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).
Teh Siew Yee is an experienced adult educator and corporate trainer specializing in business communication, workplace effectiveness, and professional development. With years of experience across both corporate and training environments, she has helped learners enhance their skills in problem-solving, collaboration, and interpersonal communication. Her ability to translate complex concepts into clear, practical strategies ensures that participants can immediately apply their learning in the workplace.
As an ACLP-certified trainer, Siew Yee delivers WSQ courses with a strong focus on learner engagement and workplace application. She integrates case studies, role-plays, and reflective exercises into her sessions, ensuring participants develop not only knowledge but also confidence in real-world contexts. By combining her corporate experience with adult education expertise, she empowers learners to improve workplace efficiency, strengthen teamwork, and achieve personal and organizational success.
Truman Ng is an AI infrastructure and cloud automation specialist with more than 20 years of experience in enterprise networking, cybersecurity, and intelligent systems integration. He holds PMP, ACTA, and Huawei HCIE certifications and has trained global corporate teams in DevOps, AI deployment, and cloud orchestration. His expertise lies in building scalable AI pipelines and integrating model fine-tuning within secure cloud-based environments.
In “Fine-Tuning LLM Models and RAG,” Truman teaches how to operationalize and optimize fine-tuned LLMs within hybrid and distributed infrastructures. His sessions focus on model deployment, GPU optimization, and the secure management of vector databases for RAG workflows. By merging AI engineering with infrastructure best practices, he enables learners to design, deploy, and maintain robust end-to-end AI systems with enterprise-level scalability.
Review
Customer Reviews (8)
- will recommend Review by Course Participant/Trainee
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. (Posted on 4/6/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recomnend Review by Course Participant/Trainee
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. (Posted on 4/6/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 12/18/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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The trainer is very knowledgeable. I hope the source code can tally more with the slides. (Posted on 10/27/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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. (Posted on 1/27/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
