WSQ - AWS Certified Machine Learning Engineer Associate Training

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.

Final Assessment

• Written Assessment (SAQ)
• Case Study

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

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

• SSG 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.

• 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

Quah Chee Yong: Quah Chee Yong is an experienced AI and data analytics specialist with more than 15 years of experience in cloud computing, data modeling, and machine learning. He has worked on enterprise-level data projects involving predictive analytics, automation, and visualization across various industries. As an ACLP-certified trainer, Chee Yong has delivered training in Python, AWS, and AI applications under national initiatives such as SGUnited and IMDA’s TechSkills Accelerator. His technical expertise covers Python, TensorFlow, and AWS SageMaker, enabling him to connect AI principles with real-world business challenges.

In this course, Chee Yong helps learners develop the knowledge and technical proficiency to design, train, and deploy machine learning models on AWS. His sessions emphasize end-to-end MLOps workflows, data preprocessing, and model optimization using SageMaker. Learners gain practical experience in applying ML solutions within AWS cloud environments, preparing them to handle production-scale AI applications confidently and effectively.

Amin Mahetar: Amin Mahetar is a senior cloud and AI architect with over 20 years of experience in cloud infrastructure, cybersecurity, and AI-driven automation. He has held leadership and consulting roles in organizations such as Cisco, GovTech, and Deutsche Bank, delivering large-scale transformation projects integrating AI with secure cloud operations. Amin is an AWS Certified Solutions Architect, Google Cloud Professional, and CISSP, recognized for his cross-domain expertise in cloud strategy, machine learning, and security governance.

In this course, Amin guides learners through advanced AWS machine learning workflows, focusing on infrastructure, automation, and model deployment. His sessions cover SageMaker pipelines, data lifecycle management, and ML model optimization using cloud-native tools. Learners benefit from his practical insights on building secure, scalable, and production-ready AI systems that integrate seamlessly with enterprise infrastructure.

Mohan Pothula: 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: Agus Salim is a seasoned IT consultant and systems engineer with more than 20 years of experience in network infrastructure, cloud solutions, and data analytics. He has worked on end-to-end enterprise projects involving AI integration, automation, and process optimization. As a certified ACLP trainer and AWS practitioner, Agus specializes in helping professionals master the intersection of AI, cloud, and data operations. His pragmatic approach to training focuses on applied learning and technical confidence.

In this course, Agus introduces learners to the practical implementation of machine learning workflows within the AWS ecosystem. His sessions cover model building, deployment, and performance evaluation using SageMaker and other AWS ML services. Learners gain the skills to design data-driven pipelines and apply machine learning models that enhance business intelligence and automation outcomes.

Truman Ng: 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.