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WSQ - AWS Certified Machine Learning Engineer Associate Training

Course Information

  • Sessions 3 days
  • Duration 24 hrs
  • Level Intermediate
  • 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
Data-Mining and Modelling
TSC Code
RET-RAN-4003-1.1

Funding Validity

Funding for this course is valid from 28 Sep 2024 to 27 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: Validate data for machine learning and ensure its accuracy, cleanliness, and integrity.
  • LO2: Review machine learning (ML) methodologies and key metrics to develop suitable ML models.
  • LO3: Deploy ML workflows and using AWS software to resolve data quality issues.
  • LO4: Propose business solutions using ML predictive analytics for solving organizational problems.

Course Brochure

Download 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 Machine Learning Engineer Associate Training

Course Code: TGS-2024049340
  • WSQ
  • SFC
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

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What's This Course About

The WSQ AWS Certified Machine Learning Engineer Associate Training equips you with essential skills to handle machine learning projects on AWS platforms. You will learn to validate and prepare data for ML, ensuring accuracy, cleanliness, and integrity. The course covers key machine learning methodologies and metrics to develop and refine effective ML models. You will also explore AWS tools and software to deploy machine learning workflows, resolve data quality issues, and implement automated CI/CD pipelines.

In addition, this training will help you utilize machine learning for predictive analytics and business solutions, addressing organizational challenges with data-driven insights. You will gain expertise in monitoring, maintaining, and securing ML solutions on AWS, optimizing infrastructure, and managing costs to ensure the successful deployment of machine learning initiatives.

Bonus: Free Practice Exams

Get exam-ready on our Practice Exam Portal — train in realistic Practice Mode and timed Exam Mode, then retake them as many times as you like before the real exam.

Start Practising →

WSQ Funding

Full Fee $1,500.00 Before GST
GST $135.00 9% of fee
Baseline Nett $885.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $585.00 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.

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

$1,500.00 (GST-exclusive)
$1,635.00 (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: Data Preparation for Machine Learning (ML)

  • Ingest and store data.
  • Transform data and perform feature engineering.
  • Ensure data integrity and prepare data for modeling.

Topic 2: ML Model Development

  • Choose a modeling approach.
  • Train and refine models.
  • Analyze model performance.

Topic 3: Deployment and Orchestration of ML Workflows

  • Select deployment infrastructure based on existing architecture and requirements.
  • Create and script infrastructure based on existing architecture and requirements.
  • Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Topic 4: ML Solution Monitoring, Maintenance, and Security

  • Monitor model inference.
  • Monitor and optimize infrastructure and costs.
  • Secure AWS resources.

Assessment

  • Written Exam
  • Case Study

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 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 a AWS account (Credit Card is required).

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
  • 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

  1. The candidate has the right to disagree with the assessment decision made by the assessor.
  2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
  3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
  4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
  5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
  6. 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.
  7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
  8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
  9. The assessor can help the candidate with writing and lodging the appeal.
  10. he assessment manager will collect information from the candidate & assessor and give a final decision.
  11. A record of the appeal and any subsequent actions and findings will be made.
  12. An Assessment Appeal Panel will be formed to review and give a decision.
  13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
  14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
  15. Please click the link below to fill up the Candidates Appeal Form.

Job Roles

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • AI Specialist
  • ML Developer
  • Data Analyst
  • Cloud Data Engineer
  • AWS Solutions Architect
  • ML Operations Engineer
  • Big Data Engineer
  • Business Intelligence Analyst
  • Predictive Analytics Specialist
  • Cloud Architect
  • ML Consultant
  • Data Engineer
  • ML Research Scientist
  • AI/ML Product Manager
  • AWS Cloud Engineer
  • DevOps Engineer (ML Ops)
  • AI Software Developer
  • Cloud Security Specialist

Trainers

Trainers

CY Quah is an ACLP-certified trainer and data science professional with extensive experience in Python, NLP, and machine learning. He has led AI training programs for SAP, Temasek Polytechnic, and IMDA under the SGUnited Mid-Career Pathways initiative, and has delivered corporate workshops on text analytics, recommender systems, and chatbot development. His expertise includes applying NLP tools such as NLTK, spaCy, and Gensim for sentiment analysis, topic modeling, and text classification.
Amin Mahetar is a cloud and DevOps engineer with over 15 years of experience in IT infrastructure, automation, and cloud solutions across AWS and Microsoft Azure environments. A Microsoft Certified Trainer (MCT) and Azure Administrator Associate, he has implemented and managed enterprise-scale cloud deployments across industries including finance, logistics, and manufacturing. His areas of expertise include virtualization, networking, identity management, and automation using PowerShell and Azure CLI. Known for his structured and hands-on training style, Amin has guided numerous professionals in achieving their Azure certifications and advancing their cloud careers.

Mohan Pothula is a data engineering and AI systems expert with more than 18 years of experience in cloud architecture, data pipelines, and intelligent automation. He has led enterprise projects for organizations such as DBS Bank and SPH Media, focusing on the integration of analytics and machine learning into business operations. With certifications in AWS, Kubernetes, and DevOps, Mohan brings deep technical expertise in deploying scalable ML solutions on cloud platforms. As an experienced trainer, he is known for his hands-on approach to complex data and AI topics.
In this course, Mohan focuses on helping learners operationalize machine learning models using AWS tools and services. His sessions emphasize feature engineering, hyperparameter tuning, and monitoring models in production. Participants gain exposure to industry-standard MLOps practices and learn to design resilient, cost-efficient ML solutions that meet business and compliance requirements.

Agus Salim is an experienced IT solutions and cybersecurity professional with a strong foundation in cloud infrastructure and project management. With over a decade of experience in systems integration, software development, and IT security across both enterprise and consulting environments, he brings a practical understanding of secure system design and deployment. His credentials include PMP, CompTIA Security+, CEH, and AWS Certified Cloud Practitioner, reflecting his balanced expertise in governance, risk management, and cloud operations. Agus has worked with leading organizations such as Citi and Check Point Software Technologies, providing hands-on technical and security support across multi-cloud platforms.

Truman Ng is a senior IT and AI systems professional with over two decades of experience in cloud computing, DevOps, and AI-powered automation. He holds multiple certifications including PMP®, AWS Certified Solutions Architect, and Huawei HCIE®. Truman has designed and delivered enterprise-level AI and cloud solutions across various sectors, integrating predictive analytics and automation frameworks into business systems. As an ACTA-certified trainer, he is known for his ability to simplify complex technical content into structured, outcome-driven learning experiences.
In this course, Truman helps learners master AWS machine learning engineering workflows, from data preparation to model deployment and monitoring. His sessions emphasize best practices in cloud-based ML pipelines, data governance, and automation. Participants gain hands-on experience in developing scalable, high-performance ML systems using AWS tools, equipping them to build and manage AI solutions that drive innovation and efficiency.

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