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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
: Generative AI Model Development and Fine Tuning
TSC Code
ICT-BAS-0048-1.1

Learning Outcomes

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

  • LO1: Implement generative AI models using deep learning architectures matched to problem requirements and evaluate model suitability.
  • LO2: Preprocess generative datasets using embeddings and tokenisation to prepare clean, structured data for model training.
  • LO3: Identify model training needs and apply optimisation techniques using benchmarks and performance metrics.
  • LO4: Train and refine generative models by evaluating weaknesses and applying targeted fine-tuning strategies.
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Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.
  • 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 - Optimizing Generative AI for Real World Deployments

Course Code: TGS-2026061312
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

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What's This Course About

WSQ Optimizing Generative AI for Real World Deployments provides learners with practical knowledge to implement and optimise generative AI models using industry-standard deep learning architectures such as GANs, VAEs, and Transformers. Participants will learn how to match model architectures to real-world problem statements, evaluate their suitability, and execute implementations using tools like TensorFlow, PyTorch, and Keras. Emphasis is placed on understanding generative theory, probabilistic modelling, and performance-based decision-making for effective deployments.

The course also equips learners with advanced data preparation techniques, including data cleaning, tokenisation, and embedding strategies essential for structured model training. Participants will explore model training needs, apply performance benchmarks, and use fine-tuning strategies to improve model outcomes. Hands-on practice in preprocessing, training, and optimising generative models makes this course ideal for aspiring AI developers, machine learning engineers, and technical professionals aiming to deliver real-world AI solutions effectively and responsibly.

WSQ Funding

Full Fee $1,100.00 Before GST
GST $99.00 9% of fee
Baseline Nett $649.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $429.00 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit

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. Click here for SkillsFuture Credit submission

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. Click here to submit UTAP

PSEA

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

To check for Post-Secondary Education Account (PSEA) eligibility for this course, Visit SkillsFuture (course code: TGS-2026061312)

  • 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 and email to us. 

Course FeeBefore Funding

$1,100.00 (GST-exclusive)
$1,199.00 (GST-inclusive)

Course Date

Course Time

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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

LU 1: Generative AI Theory

T1: Probability theory and statistics (e.g., latent variables, probabilistic modelling)

T2: Deep learning theory and algorithms (e.g., GANs, VAEs, Transformers)

T3: Machine learning libraries (e.g., TensorFlow, PyTorch, Keras)

T4: Implement generative models based on existing architectures

T5: Analyse problem statements and requirements to select and implement appropriate generative models

LU 2: Generative AI Data Preparation

T1: Common dataset formats and evaluation methodologies for generative tasks

T2: Data pre-processing, de-duplication and cleaning techniques (including understanding of training data requirements for AI models, common data quality issues)

T3: Embeddings and tokenisation

T4: Preprocess and prepare data for generative training (e.g., clean and format datasets, use libraries (e.g., Pandas, NumPy) for data manipulation, split data into training, validation and test sets)

LU 3: Generative AI Model Training

T1: Optimisation techniques for training neural networks

T2: Parallel cluster training and inference

T3: Loss functions and evaluation metrics for generative tasks

T4: Train generative models on benchmark datasets

LU 4: Generative AI Model Fine Tuning

T1: Fine-tuning techniques (e.g., supervised fine-tuning, parameter-efficient fine-tuning, perform inference)

T2: Identify limitations and propose initial improvements to models

Assessment

  • Written Exam
  • Practical Exam

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

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:

TBD

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • AI Developer
  • Machine Learning Engineer
  • Data Scientist
  • Deep Learning Specialist
  • AI Research Assistant
  • Software Engineer (AI)
  • AI Solutions Architect
  • NLP Engineer
  • AI Systems Integrator
  • Data Engineer
  • Computer Vision Engineer
  • Model Validation Analyst
  • AI Innovation Specialist
  • AI Product Developer
  • Python Developer (AI Focus)
  • Data Analyst (AI Track)
  • AI Technical Consultant
  • Applied Scientist (Generative AI)
  • AI Deployment Specialist
  • Research Engineer

Trainers

Trainers

Dr. Alfred Ang: Dr. Alfred Ang is a distinguished expert in AI, digital transformation, and workforce development with over 20 years of experience in industry and adult education. As Chief Instructional Designer, Chief Technology Officer, and Chief Information Officer of Tertiary Infotech Pte Ltd, he has spearheaded the design and deployment of more than 500 WSQ- and IBF-accredited courses, aligning with national and international industry standards. His extensive technical portfolio spans generative and agentic AI, cloud computing, cybersecurity, blockchain, and robotics. With a PhD from the National University of Singapore and advanced certifications including PMP®, CSM®, AWS AI Engineer, Microsoft Azure Data Scientist, and SCS Certified Senior AI Ethics Professional, Dr. Ang combines academic depth with practical expertise to deliver impactful AI solutions In addition to his leadership role, Dr. Ang has driven numerous industrial and in-house projects focused on real-world AI deployment, such as multimodal AI platforms, LLM-powered robotics, AI-driven automation workflows, and curriculum-generation systems powered by agentic AI. He has also consulted on workplace learning projects, guiding companies in adopting AI-powered business solutions while ensuring scalability, transparency, and measurable impact. As a mentor to university and polytechnic interns, he has cultivated the next generation of AI professionals, preparing them for careers in cybersecurity, robotics, and intelligent automation. Passionate about lifelong learning and practical innovation, Dr. Ang brings a unique perspective to optimizing generative AI for real-world deployments, integrating technical mastery with ethical and sustainable strategies

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