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Course Details
Course Details
What You'll Learn
Topic 1: Evaluating Agentic AI Tools for Product Research and Development
Topic 2: Building and Optimising Agentic AI Product Development Workflows
Topic 3: Deploying and Evaluating Agentic AI-Powered Product Solutions
Course Info
Prerequisite
Basic Python or C#, data handling and familiarity with cloud services are recommended. Data engineering courses also benefit from SQL knowledge.
Software Requirement
Install and test the listed software before class. Confirm the required version and account or licence access in your joining instructions. Cloud and AI services may charge subscription or usage fees.
Hardware Requirement
Bring a Windows or Mac laptop supported by the listed software, with a working keyboard and sufficient storage for course exercises.
Bring your own computer and charger, with a reliable internet connection and permission to install the required software. For live online classes, also prepare a microphone or headset.
If you need help arranging a suitable device, contact us before the class to confirm availability.
Job Roles
Job Roles
- Product Manager
- Product Owner
- AI Product Manager
- Product Development Engineer
- Digital Product Manager
- UX/UI Designer
- User Researcher
- AI Implementation Consultant
- Product Marketing Manager
- Solution Architect
- Web Developer
- Data Analyst
- Software Engineer
- Business Analyst
- Innovation Manager
- Project Manager
- AI Researcher
- Customer Insights Analyst
- Quality Assurance Engineer
- R&D Manager
Trainers
Trainers
Dr. Muhammed Siraj is a cybersecurity specialist and academic with 15 years of experience, serving as Senior Lecturer in the Department of Informatics and Director of Graduate and Continuous Education at Academic City University in Accra. He holds a PhD in Security in Computing from Universiti Putra Malaysia, where his research focused on blockchain security, an MSc in Information and Communication Technology from Aalborg University in Denmark, and a degree from the University of Ghana. He is a licensed independent assessor with Ghana's Cyber Security Authority, holds leadership roles in national cybersecurity and privacy professional bodies, and is certified as a Certified Ethical Hacker (CEH), Computer Hacking Forensic Investigator (CHFI) and Certified Data Protection Supervisor (CDPS). Before joining Academic City, he served as Acting Director of ICT and Data Protection Supervisor at Wisconsin International University College. His expertise spans penetration testing, digital forensics, incident response, malware analysis, cryptography, cloud and IoT security, zero trust architecture, cyber threat intelligence, data protection, and governance, risk and compliance.
Dr. Siraj's research interests cover blockchain, cybersecurity, distributed computing, consensus protocols and machine learning, with published work on blockchain consensus security, smart-contract privacy preservation for sensitive records in the cloud, and new consensus mechanisms. This background gives him a distinctive perspective on artificial intelligence: as organisations adopt AI tools, agents and data-driven automation, he helps learners understand how to protect the data these systems depend on, recognise new attack surfaces, and put sound governance, privacy and access controls in place so that AI can be used safely and responsibly. In the classroom he connects technical practice with regulation, privacy and human factors, drawing on real-world security and data-protection cases to show how concepts apply in professional settings, and his experiential, hands-on teaching style has made him a trusted trainer for students and working professionals alike.
Turning machine learning ideas into reliable, automated systems is the thread connecting much of Dr. Alfred Ang's work. Dr. Ang is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and serves as Chief Technology Officer of Tertiary Infotech Academy Pte Ltd in Singapore, the training organisation behind Tertiary Courses. For data science foundations, Dr. Ang built interactive Python and Streamlit labs that guide beginners through data mining and machine learning, and an AI inventory forecasting agent that runs a local language model to analyse stock and compute replenishment. For generative AI operations, Dr. Ang has built retrieval-augmented chatbots backed by Supabase, Pinecone and Qdrant vector databases, agent workflows in n8n, and AI-powered content platforms on the Claude Agent SDK. Dr. Ang's engineering habits also map closely onto MLOps: every project lives under Git source control across more than 300 public repositories on GitHub, iOS apps are built, signed and submitted automatically through GitHub Actions, and web platforms such as the course-registration system behind the Tertiary Courses websites in Singapore, Malaysia and Ghana are deployed in containers with automated rebuilds. Certification preparation is familiar territory too, through a full-stack practice-exam platform with timed modes and AI-assisted question generation, plus native mobile apps for practising on the go. Azure-specific services are not the centre of the public portfolio, so the course links these hands-on lessons to Azure's tooling, with Dr. Ang working end to end across front end, back end, databases, cloud deployment and mobile.
This DP-100 Azure Data Scientist Associate Training follows the official exam domains, and Dr. Ang uses that practical background to make each one tangible. The first domain covers designing and implementing MLOps infrastructure: creating and managing a Machine Learning workspace, datastores and compute targets, configuring identity and access, managing data assets, environments and components, sharing assets through registries, provisioning resources with Bicep and the Azure CLI, automating with GitHub Actions and managing source control with Git. The second domain addresses the model life cycle: tracking experiments with MLflow, using automated ML and notebooks, tuning hyperparameters, running training as jobs and pipelines, managing distributed training, registering and evaluating models with responsible AI principles, deploying real-time and batch endpoints with safe rollout and rollback, and monitoring production models for data drift. The third domain introduces GenAIOps infrastructure with Microsoft Foundry resources, project environments and access management. Throughout, Dr. Ang explains the reasoning behind each service so that learners can handle unfamiliar scenario questions, and draws on real experience of automated pipelines, version control and monitoring to show why these practices matter outside the exam. Having built a practice-exam platform, Dr. Ang also coaches exam technique: reading scenarios carefully, eliminating distractors and using practice results to focus revision. Sessions combine explanation, guided demonstrations and timed practice questions with answer reviews. Learners leave with a clear revision plan and confidence for the exam. To explore the machine learning labs and exam platform behind this course, visit https://github.com/alfredang
Review
Customer Reviews (7)
- 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 9/2/2025)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 - Exceeded my expectations Review by Course Participant/Trainee
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The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 8/19/2025)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 - Knowledgeable trainer Review by Course Participant/Trainee
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Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 7/24/2025)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 - Excellent course Review by Course Participant/Trainee
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Really enjoyed the training. The examples were relevant and the pace was just right. (Posted on 3/7/2025)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 - Highly recommended Review by Course Participant/Trainee
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Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 2/3/2025)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
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