WSQ - Google Professional Machine Learning Engineer Training

Topic 1 Google Cloud Big Data and Machine Learning Fundamentals

• Data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
• Design streaming pipelines with Dataflow and Pub/Sub and design streaming pipelines with Dataflow and Pub/Sub.
• Options to build machine learning solutions on Google Cloud.
• Machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.

Topic 2 How Google does Machine Learning

• Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing any code
• Best practices for implementing machine learning on Google Cloud
• Leverage Google Cloud tools and environment to do ML
• Responsible AI best practices

Topic 3 Launching into Machine Learning

• Improve data quality and perform exploratory data analysis
• Build and train AutoML Models using Vertex AI and BigQuery ML
• Optimize and evaluate models using loss functions and performance metrics
• Create repeatable and scalable training, evaluation, and test datasets

Topic 4 TensorFlow on Google Cloud

• Create TensorFlow and Keras machine learning models and describe their key components.
• Use the tf.data library to manipulate data and large datasets.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation.
• Train, deploy, and productionalize ML models at scale with Vertex AI.

Topic 5 Feature Engineering

• Describe Vertex AI Feature Store and compare the key required aspects of a good feature.
• Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
• Discuss how to preprocess and explore features with Dataflow and Dataprep.
• Use tf.Transform.

Topic 6 Machine Learning in the Enterprise

• Describe data management, governance, and preprocessing options
• Identify when to use Vertex AutoML, BigQuery ML, and custom training
• Implement Vertex Vizier Hyperparameter Tuning
• Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI

Topic 7 Production Machine Learning Systems

• Compare static versus dynamic training and inference
• Manage model dependencies
• Set up distributed training for fault tolerance, replication, and more
• Export models for portability

Topic 8 Machine Learning Operations (MLOps)

• Core technologies required to support effective MLOps.
• Adopt the best CI/CD practices in the context of ML systems.
• Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
• Implement reliable and repeatable training and inference workflows.
• ML Pipelines on Google Cloud

Final Assessment 

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

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 can download and install the following software:

• Google Cloud

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.

• Data Scientist
• Machine Learning Engineer
• AI Engineer
• Data Analyst
• Software Engineer
• Cloud Solutions Architect
• Research Scientist
• Application Developer
• Big Data Engineer
• Business Intelligence Developer
• Robotics Engineer
• Quantitative Analyst
• Systems Analyst
• Product Manager
• Technical Program Manager

Amin Mahetar:
Amin Mahetar is a data science and AI engineer with over 15 years of experience in machine learning, predictive analytics, and cloud-based model deployment. A Google Cloud Certified Professional and WSQ-accredited trainer, he has designed and implemented end-to-end AI solutions across finance, logistics, and education sectors. His expertise includes TensorFlow, PyTorch, and Google Vertex AI, with a strong focus on applying deep learning to solve complex business challenges. Amin is known for bridging theoretical AI concepts with practical, production-level implementation in real-world systems.

In “Google Professional Machine Learning Engineer Training (Synchronous e-Learning),” Amin guides participants through the full ML lifecycle — from data preparation and model training to evaluation and deployment on Google Cloud. His sessions emphasize MLOps practices, scalable AI pipeline design, and ethical AI development. Learners benefit from his hands-on approach and industry-driven insights that prepare them to confidently tackle the certification exam and apply advanced ML techniques in enterprise environments.

Ben Law Beng Tjin: Ben Law is an experienced data analytics and cloud computing specialist with over two decades of experience in IT infrastructure, machine learning systems, and enterprise data solutions. He has led numerous AI implementation projects involving cloud-based predictive analytics, big data engineering, and data visualization. As an ACLP-certified trainer, Ben combines his technical depth with a structured and engaging teaching style that simplifies complex topics for learners of all backgrounds.

In “Google Professional Machine Learning Engineer Training (Synchronous e-Learning),” Ben provides a deep understanding of machine learning models, data pipelines, and Google Cloud ML services. His sessions cover AutoML, TensorFlow Extended (TFX), and deployment strategies on GCP. Through practical exercises and real-world case studies, he enables learners to design, train, and operationalize machine learning models aligned with Google Cloud’s best practices.

Truman Ng: Truman Ng is a senior IT consultant and AI systems architect with more than 20 years of experience in cloud infrastructure, cybersecurity, and intelligent systems integration. A PMP, ACTA, and Huawei HCIE-certified professional, he has trained global teams in AI deployment, DevOps, and cloud automation. His expertise lies in designing scalable architectures for AI and ML solutions across hybrid and multi-cloud environments.

In “Google Professional Machine Learning Engineer Training (Synchronous e-Learning),” Truman focuses on integrating Google Cloud infrastructure with machine learning workflows. His sessions emphasize data engineering, distributed training, and model orchestration using Vertex AI and BigQuery ML. By combining system-level engineering with applied AI concepts, he helps learners master the technical and architectural skills required for building robust ML solutions in production.

Quah Chee Yong: Quah Chee Yong is a cloud computing and data analytics expert with more than 15 years of experience in IT systems, business intelligence, and machine learning deployment. A Google Cloud Certified Professional and ACLP-qualified educator, he has helped organizations modernize their analytics infrastructure through cloud-native solutions. His strong background in AI application design and data pipeline automation positions him as an effective mentor for professionals pursuing advanced machine learning certification.

In “Google Professional Machine Learning Engineer Training (Synchronous e-Learning),” Chee Yong teaches learners how to design and operationalize end-to-end machine learning solutions on Google Cloud. His sessions cover feature engineering, model tuning, and CI/CD integration for ML systems. With a focus on practical implementation and performance optimization, he equips participants with the skills to develop scalable and efficient AI applications aligned with enterprise requirements.

Agus Salim: Agus Salim is an IT systems engineer and data analytics practitioner with over 15 years of experience in network administration, software engineering, and machine learning applications. A WSQ-accredited trainer and Microsoft Certified Professional, he has trained professionals in data analytics, AI integration, and cloud computing technologies. His teaching approach emphasizes real-world use cases and applied learning to ensure learners gain both technical and practical expertise.

In “Google Professional Machine Learning Engineer Training (Synchronous e-Learning),” Agus introduces participants to key ML concepts, data preparation workflows, and model deployment using Google Cloud tools. His sessions focus on bridging programming fundamentals with AI model development using Python and TensorFlow. Through structured exercises and industry-relevant examples, he helps learners build a solid foundation in machine learning engineering and successfully prepare for Google’s professional certification.