Our Courses
Three courses at different scopes. Each addresses a distinct part of the AI engineering stack, and each can be taken independently.
← Back to HomeHow We Structure the Work
Each course runs on a concept-then-exercise rhythm. Sessions open with a walkthrough of the relevant idea, then move into structured exercises where the idea is applied to a specific problem. The exercises are chosen because they tend to surface the points that look clear in theory but are less clear when you try to implement them.
Where applicable, exercises run on shared practice environments so time isn't lost on local setup. All materials are provided ahead of sessions in written form. The post-cohort review is built into how we operate — every content section is assessed against participant outcomes after each run, and sections that aren't working are revised before the next cohort.
Prerequisites are stated plainly on each course page. We would rather someone asks us about fit before enrolling than enrols and finds the content too advanced or not advanced enough for their current stage.
Software Fundamentals for AI Engineering
A seven-week course covering version control, testing, code reviews, and basic deployment — the surrounding engineering craft that turns an experimental notebook into a reliable system. Exercises are structured around small AI-adjacent projects so learners practise in the kind of setting where they will later work. Suitable for self-taught developers moving into team contexts.
What You Work On
- Git branching, pull requests, and code review practice
- Writing tests for AI-adjacent Python code
- Basic deployment: packaging and environment management
- Working in a shared codebase with other learners
Process Steps
Concept session — weekly walkthrough of the core idea
Structured exercise on the week's topic with feedback
Review session — common issues from exercises discussed
Final project: a small AI-adjacent repository following all covered practices
MLOps in Practice
A ten-week course on operating machine learning systems in production. Topics include data pipelines, model registries, monitoring, and safe rollout practices. Weekly sessions combine concept walkthroughs with hands-on exercises on shared practice environments. Students present a small MLOps design at the end of the programme, with written peer feedback included as part of the mentor review.
What You Work On
- Designing and running data ingestion pipelines
- Model registry setup, versioning, and artifact management
- Monitoring dashboards and drift detection approaches
- Rollout strategies: canary, shadow, and staged deployment
- Final design presentation with structured peer feedback
Process Steps
Weekly concept walkthrough on shared practice environments
Hands-on exercise with mentor check-in during the week
Peer discussion session — common patterns across cohort work reviewed
Final week: MLOps design presentation, written peer feedback, mentor review
Short Introduction to Prompt and Context Design
A three-evening short course for learners who work with large-language-model tools at their jobs and wish to understand prompt design, context curation, and evaluation of outputs. No prior machine-learning experience is required. Sessions emphasise careful reading of outputs and healthy scepticism about confident-seeming results.
What You Work On
- Prompt structure: role, instruction, context, output format
- Context curation — what to include and what to leave out
- Recognising unreliable outputs without external verification
- Iterating on prompts systematically rather than by guesswork
Process Steps
Evening one: Prompt anatomy and context decisions
Evening two: Output evaluation and iteration strategies
Evening three: Applying the framework to participants' own work contexts
Which Course Fits Your Stage
A quick reference to help you decide based on what you need right now.
| Feature | Fundamentals | MLOps | Prompt |
|---|---|---|---|
| Duration | 7 weeks | 10 weeks | 3 evenings |
| Price | RM 1,480 | RM 2,780 | RM 260 |
| Coding experience needed | |||
| ML experience needed | |||
| Shared practice environment | |||
| Peer feedback component | |||
| Best for | Self-taught devs entering team contexts | Engineers running or building ML in production | Any professional using LLM tools at work |
What Every Course Includes
Data Privacy
Participant data is not shared with advertising platforms. Minimal collection, stated clearly.
Post-Cohort Revision
Content sections are reviewed and revised based on participant outcomes after every cohort run.
Instructor Access
Questions about specific content go to the instructor who wrote it, not a support tier.
Written Materials
All session content is provided in written form ahead of sessions. No surprise-only-in-video content.
Course Fees
All prices in Malaysian Ringgit. Corporate invoicing available for employer-funded enrolment.
Not sure which course is the right fit?
Write to us with your background and what you are trying to accomplish. We will tell you honestly which course fits or whether none of them are the right match at this point.
Ask Us Directly