Course Details
Course Details
What You'll Learn
Topic 1: Solution Design and Architecture
Topic 2: Claude Models, Prompting, and Context Engineering
Topic 3: Integration
Topic 4: Evaluation, Testing, and Optimisation
Topic 5: Governance, Safety, and Risk Management
Topic 6: Stakeholder Communication and Lifecycle Management
Topic 7: Developer Productivity and Operational Enablement
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
- Experience designing or building LLM-powered applications, with working knowledge of Python or TypeScript and REST APIs
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- A Claude Developer Platform account with API credits – required for the Claude API, RAG and MCP labs (pay as you go)
- A Claude Pro (or higher) subscription – required for Claude Code
- Python 3.10 or later – free
- Node.js (LTS) – free, for the MCP server labs
- Visual Studio Code – free
Hardware: Windows PC or Mac laptop
How to Apply HRD Corp Funding
This course is HRD Corp claimable for employers registered with HRD Corp, under the HRD Corp Claimable Courses scheme (SBL-Khas). Your company applies for the grant before the class; we submit the training provider claim after it.
- Check your levy balance - your company must be registered with HRD Corp and have enough levy balance to cover the course fee.
- Request a quotation - contact us for the quotation, course content and trainer profile; you attach these to the grant application.
- Apply for the grant in eTRiS before the class - log in to your employer eTRiS account, go to Application > Grant > Apply Grant, choose the scheme HRD Corp Claimable Courses (SBL-Khas), select us as the training provider and this course, add your trainees, attach the quotation, course content and trainer profile, and submit.
- Submit in time - for public classes, submit at least 3 calendar days before the class (15 June to 31 December 2026) and at least 14 days before from 1 January 2027; in-house classes need 14 days. The training must start within 90 days of that advance submission period.
- Send us the grant approval - once HRD Corp approves the application, email us the grant ID so we can confirm your seats.
- Attend the class - trainees sign the daily attendance (T3) form.
- Claim after the class - we submit the training provider claim in eTRiS with the attendance and invoice; you then complete the employer claim and sign the JD/14 form (manager level or above). Claims must be submitted within 6 months of the training.
More details: our HRD Corp Claimable Courses page and the HRD Corp employer guidelines.
Job Roles
Job Roles
- AI Solutions Architect
- Solutions Architect
- Enterprise Architect
- Technical Architect
- Cloud Architect
- AI Engineer
- Senior Software Engineer
- Machine Learning Engineer
- Generative AI Lead
- Forward Deployed Engineer
- Solutions Engineer
- Platform Engineer
- Technical Lead
- Engineering Manager
- AI Consultant
- AI Product Manager
- AI Governance Lead
- Head of AI
Trainers
Trainers
Dr. Alfred Ang approaches machine learning engineering from the perspective of someone who has to put models and AI services into products people actually use. A full-stack AI developer, Agentic AI solution architect, corporate trainer and CTO of Tertiary Infotech Academy Pte Ltd in Singapore, Dr. Ang builds intelligent agents and scalable solutions end-to-end across front end, back end, databases, cloud deployment and native mobile apps, with more than 300 public repositories on GitHub. Google technology appears throughout that portfolio. The n8n-ragdemo project feeds uploaded PDFs into a vector store and answers questions through a Telegram AI agent powered by Google Gemini, while a bakery training lead-magnet site uses an n8n and Gemini chatbot to compare context-stuffing with retrieval. The logomaker app generates logos with Gemini image models, and native Android apps use Google Maps and on-device ML Kit features such as document edge detection, optical character recognition and background removal. For core machine learning, data-mining-ml-labs provides interactive Python and Streamlit labs covering data preparation, feature selection and model training for beginners, and n8n-inventorycontrol runs a demand-forecasting agent on Google's open Gemma model served locally through Ollama. Dr. Ang also built ai-exams, a full-stack certification practice-exam platform with timed exam modes and AI-assisted question generation, reflecting a strong interest in how professionals prepare for cloud certifications. This combination of practical deployment and exam-preparation experience gives Dr. Ang a clear view of what the certification expects.
Google Professional Machine Learning Engineer Training uses that practical grounding to make a broad syllabus manageable. The course moves through Google Cloud big data and machine learning fundamentals, how Google does machine learning, launching into machine learning, TensorFlow on Google Cloud, feature engineering, machine learning in the enterprise, and production machine learning systems. Dr. Ang links each area to decisions an engineer actually faces: choosing between pre-built APIs, AutoML and custom training, designing data pipelines that will not leak information into evaluation, selecting and engineering features, tuning and validating models, and serving, monitoring and retraining them on Vertex AI once they are live. Responsible AI, fairness and explainability are treated as design requirements rather than afterthoughts. Learners work through demonstrations, guided notebooks and scenario-based questions in the style of the certification exam, and Dr. Ang explains why each wrong option fails, which tends to be where real understanding forms. Examples from deploying Gemini-based chatbots and on-device ML features help illustrate trade-offs between accuracy, latency, cost and maintainability that the exam likes to test. The course suits data scientists, ML engineers, developers and cloud practitioners preparing for the certification, ideally with some Python and basic statistics. Learners who would like to explore the machine learning labs and Gemini projects mentioned during the course can visit Dr. A structured revision plan and guidance on official practice resources help learners continue preparing confidently after the course. Ang's portfolio at https://github.com/alfredang.
Review
Customer Reviews (9)
- Worth every cent Review by Course Participant/Trainee
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Very engaging session. The trainer patiently answered all our questions and gave useful tips. (Posted on 14/06/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Very satisfied Review by Course Participant/Trainee
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Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 21/05/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Good course materials Review by Course Participant/Trainee
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I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 13/01/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Highly recommended Review by Course Participant/Trainee
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The trainer was knowledgeable and explained the concepts clearly with real-world examples. (Posted on 14/06/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Fantastic experience Review by Course Participant/Trainee
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The course materials were detailed and easy to reference afterwards. Great value for money. (Posted on 13/10/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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