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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
Assessment
- Written Exam
- Practical Exam
Course Info
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 the following for this course:
- A Google Cloud account – free trial; a credit card is required to sign up
- Google Cloud CLI – free
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2023040476.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
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
Dr. Alfred Ang works across the major cloud platforms, and he brings that comparative view to Google Cloud's machine learning stack. His certifications include AWS Certified Solutions Architect, AI Practitioner and Cloud Practitioner, and Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102), so he knows the scenario-based style of professional cloud certification well. He builds with Google's AI tooling directly too: the Google Gemini Agent SDK is among the agent frameworks he uses, n8n-ragdemo is a no-code RAG demo in which a Telegram AI agent powered by Google Gemini answers questions from uploaded documents, and bakerycourses-demo pairs a landing site with an n8n and Gemini course chatbot. As founder of Tertiary Courses and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, he is a full-stack AI developer and Agentic AI solution architect whose work spans data engineering, machine learning, Kubernetes and DevOps. He is also an IBM certified instructor for the AI Practitioners course and completed a Graduate Diploma in Intelligent Robotics Systems at NUS-ISS. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and has published more than 20 peer-reviewed papers and co-invented more than 20 US patents. His data-mining-ml-labs repository, a set of interactive Python and Streamlit labs, shows the lab-first way he teaches machine learning.
In WSQ - Google Professional Machine Learning Engineer Training, Dr. Ang prepares learners to design, build and productionise machine learning solutions on Google Cloud and to sit the certification exam with confidence. The course begins with big data and machine learning fundamentals: the data-to-AI lifecycle on Google Cloud, streaming pipelines with Dataflow and Pub/Sub, and the options for building ML solutions. It then covers how Google does machine learning with Vertex AI, including AutoML, best practices and responsible AI, before launching into machine learning with data quality checks, exploratory analysis, BigQuery ML and model evaluation using loss functions and performance metrics. The TensorFlow topic builds Keras models with the Sequential and Functional APIs, uses tf.data for large datasets and trains and deploys at scale on Vertex AI, and the feature engineering topic introduces Vertex AI Feature Store and techniques for better features. He maps Google services to their AWS and Azure equivalents, which helps learners who already know another cloud and reinforces the architecture reasoning the exam rewards. His Kubernetes and DevOps experience informs the discussions on pipelines, reproducibility and monitoring, and his AI ethics and governance training underpins the responsible AI content. Sessions combine hands-on labs with exam-style scenario questions so learners practise both building and choosing. As an ACTA-certified trainer and DACE-certified curriculum developer, he structures revision around the exam guide so learners know where to focus. To explore his machine learning and cloud projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Quah Chee Yong is a cloud computing and data analytics expert with experience in IT systems, business intelligence and machine learning deployment. A Google Cloud Certified Professional and ACLP-qualified educator, he has helped organisations modernise their analytics infrastructure through cloud-native solutions. His background in AI application design and data pipeline automation complements his data science work in Python, natural language processing and machine learning, and he has led AI training programmes for SAP, Temasek Polytechnic and IMDA under the SGUnited Mid-Career Pathways initiative.
In WSQ Google Professional Machine Learning Engineer Training, he teaches learners to design and operationalise end-to-end machine learning solutions on Google Cloud. Participants cover the data-to-AI lifecycle and streaming pipelines with Dataflow and Pub/Sub, then use Vertex AI and BigQuery ML to build, train and evaluate AutoML models, following responsible AI best practices. They create TensorFlow and Keras models, handle large datasets with tf.data, deploy models at scale with Vertex AI, and explore feature engineering with Vertex AI Feature Store. His focus on practical implementation and performance optimisation prepares participants for the certification and for scalable enterprise ML work.
Amin Mahetar is a data science and AI engineer specialising 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 the finance, logistics and education sectors, with expertise in TensorFlow, PyTorch and Google Vertex AI and a strong focus on applying deep learning to complex business challenges. He is known for bridging theoretical AI concepts with production-level implementation, and is also a Microsoft Certified Trainer and Azure Administrator Associate with a background in cloud infrastructure and automation.
In this WSQ course, Amin guides participants through the full machine learning lifecycle on Google Cloud, from data preparation and model training to evaluation and deployment. Learners cover Google Cloud big data and machine learning fundamentals, the data-to-AI lifecycle, streaming pipelines with Dataflow and Pub/Sub, the options for building ML solutions, and the key workflow steps with Vertex AI. His sessions emphasise MLOps practices, scalable AI pipeline design and ethical AI development. Participants benefit from his hands-on approach and industry-driven insight, leaving well prepared to tackle the Google Professional Machine Learning Engineer exam and apply advanced ML techniques in enterprise environments.
Ben Law is a 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 visualisation, giving him first-hand insight into how machine learning delivers value in enterprise settings. As an ACLP-certified trainer, Ben combines 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, Ben provides a deep understanding of machine learning models, data pipelines and Google Cloud ML services. Learners explore the data-to-AI lifecycle on Google Cloud, design streaming pipelines with Dataflow and Pub/Sub, and use Vertex AI to build, train and deploy models, including AutoML. His sessions also cover TensorFlow Extended (TFX) and deployment strategies on Google Cloud, through to production machine learning systems and MLOps. With practical exercises and real-world case studies, he enables learners to design, train and operationalise machine learning models in line with Google Cloud best practice, preparing them to approach the certification exam with confidence.
Agus Salim is an IT consultant and systems engineer specialising in the intersection of AI, cloud and data operations, with end-to-end enterprise project experience involving AI integration, automation and process optimisation. He teaches machine learning workflows within the AWS ecosystem, covering model building, deployment and performance evaluation with SageMaker, and has worked with organisations such as Citi and Check Point Software Technologies. An ACLP-certified trainer, he holds PMP, CompTIA Security+, CEH and AWS Certified Cloud Practitioner.
In WSQ - Google Professional Machine Learning Engineer Training, Agus brings his machine learning workflow and cloud experience to the Google Cloud platform. He guides learners through the data-to-AI lifecycle on Google Cloud, big data and machine learning fundamentals, designing streaming pipelines with Dataflow and Pub/Sub, and building machine learning pipelines with Vertex AI and AutoML. His pragmatic, applied approach helps participants understand how data preparation, model development and deployment connect across a complete solution. Learners build technical confidence across the critical areas of the certification and leave better prepared to design data-driven machine learning solutions.
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