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WSQ Google Professional Machine Learning Engineer Certification Prep (Synchronous e-Learning)

Course Information

  • Sessions 4 days
  • Duration 32 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
Cloud Computing
TSC Code
ICT-DIT-4020-1.1

Learning Outcomes

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

  • LO1: Assess and draft Google Cloud solution specifications to meet expected business machine learning requirements.
  • LO2: Develop implementation plans and resolve issues for Google Cloud machine learning solutions .
  • LO3: Develop and review processes for metrics associated with Google Cloud mchine learning solution implementation.
Download Course Brochure

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 - Google Professional Machine Learning Engineer Training

Course Code: TGS-2023040476
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

1 Review(s) | Add Your Review

What's This Course About

Embark on a transformative journey towards becoming a Google Professional Machine Learning Engineer. Our comprehensive preparation course is meticulously designed to cover all the critical facets of machine learning, ensuring you gain the expertise needed to pass the certification exam confidently. By engaging with this course, you will dive deep into the development of scalable machine learning models, understanding complex data pipelines, and deploying robust ML projects using Google Cloud technologies. This certification signifies to employers that you possess the acumen to leverage machine learning in a way that drives powerful, innovative solutions.

This advanced training program goes beyond the fundamentals, providing insights into machine learning algorithms, model optimization, and problem-solving techniques crucial for real-world applications. You will learn how to approach machine learning engineering with an ethical and socially responsible lens while mastering the skills to build, test, and deploy AI systems that are scalable and reliable. Our curriculum is crafted to ensure that upon completion, you will not only be prepared for the Google Professional Machine Learning Engineer exam but also equipped to propel your career forward in the thriving field of AI and machine learning.

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 $1,600.00 Before GST
GST $144.00 9% of fee
Baseline Nett $944.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $624.00 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

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form and email to us. 

Course FeeBefore Funding

$1,600.00 (GST-exclusive)
$1,744.00 (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 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)

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 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:

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 SkillsFuture Singapore's (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 SkillsFuture Singapore's (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), SSG 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

  • 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.

Job Roles

Job Roles

  • 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

Trainers

Trainers

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.

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 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.

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.
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.

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