The Programmes in Full
Detailed descriptions, process steps and pricing for each offering — so you can compare them properly before getting in touch.
Back to HomeHow the School Structures Learning
The Terrace Compute Grid is the design principle behind all Suria Kod programmes. Each level or module sits one step below the last, building on what came before. You do not advance until the current level is completed to the required standard — and you can retake a level if the assessed work does not get there the first time.
Cohorts are small. Mentors are practitioners. Office hours run in Malaysian time. These are structural decisions that affect every programme on this page.
Assessed Exits
Each level closes with work that must meet a stated standard before progression.
Capped Cohorts
Twenty learners maximum. Mentors know the people in the room.
MYT Office Hours
Five evenings a week in Malaysian time during active cohorts.
Published Data
Completion rates by level are published so applicants can judge difficulty themselves.
Six-Level AI Development Programme
20 weeks · ~12 hours/week · cohort max 20
A twenty-week programme structured as six levels, each closing with an assessed piece of work that must be completed before the next begins. Levels cover Python for data work, statistics, classical models, neural networks, deployment and a supervised final project.
Mentors hold office hours five evenings a week and cohort sizes are capped at twenty. The school publishes how many learners finish each level so applicants can judge the difficulty for themselves. About twelve hours a week, and levels may be retaken.
What You Can Do at the End of Each Level
- 1Write clean Python code for loading, transforming and inspecting tabular datasets
- 2Describe distributions, test hypotheses and interpret results without overstating them
- 3Select, train and evaluate classical models for a stated problem; explain why you chose them
- 4Build and tune a neural network; describe its failure modes honestly
- 5Containerise a model and deploy it to a working endpoint that handles real requests
- 6Produce a complete, documented AI project that a technical reviewer can assess independently
Key Points
- Six levels — Python, statistics, classical models, neural networks, deployment, final project
- Each level exit assessed before the next opens
- Completion data published per level
- Cohort capped at twenty
- Mentor office hours five evenings/week MYT
- Levels may be retaken
Key Points
- Ingestion, storage formats, batch and streaming pipelines
- Data quality checks and dataset versioning
- Final two weeks: keep your pipeline running in production
- Suited to developers and analysts moving toward ML teams
- 6–8 hours per week
- Available as standalone or alongside the main programme
Data Engineering for AI Teams
7 weeks · 6–8 hours/week · developer or analyst background
A seven-week module on the work that sits underneath models: ingestion, storage formats, batch and streaming pipelines, data quality checks and the versioning of datasets. Learners build a pipeline that feeds a training job on a schedule and keep it running for the final two weeks, which is where most of the learning happens.
Suited to developers and analysts moving toward machine learning teams. The module is available as a standalone course — you do not need to have completed the six-level programme first.
Module Process
- 1Weeks 1–2: Ingestion patterns and storage format selection for different data types
- 2Weeks 3–4: Batch and streaming pipeline design; data quality checks at each stage
- 3Week 5: Dataset versioning and connecting the pipeline to a scheduled training job
- 4Weeks 6–7: Keep the pipeline running; diagnose and fix what breaks — the real learning phase
Portfolio Review & Mentoring Session
90 minutes · 1-to-1 · open to all
A ninety-minute one-to-one review of a learner's existing projects with a working practitioner, covering code structure, documentation, the clarity of the problem statement and how the work reads to a technical reviewer.
The learner receives written notes with specific changes to make, in priority order, and a candid view of where the portfolio currently stands. Open to people who have never studied with the school — no prior enrolment required.
Session Structure
- 1Before the session: share your portfolio or project files so the reviewer can read them in advance
- 2First 30 min: code structure, documentation quality and problem statement clarity
- 3Next 40 min: how the work reads to a technical reviewer; specific weaknesses and what to fix
- 4Final 20 min: questions, priority list discussed, next steps agreed
- 5After the session: written notes delivered with changes listed in priority order
What Gets Reviewed
- Code structure and readability
- Documentation completeness
- Problem statement clarity
- How the work reads to a technical reviewer
- Prioritised written notes delivered after
- No prior Suria Kod enrolment required
Choosing the Right Programme
Use the table below to match your current situation to the right option.
| Consideration | 6-Level Programme | Data Engineering | Portfolio Review |
|---|---|---|---|
| Best for | New to AI, wants a full structured path | Developer/analyst moving to ML teams | Has existing projects, wants honest feedback |
| Duration | 20 weeks | 7 weeks | 90 min |
| Weekly commitment | ~12 hrs | 6–8 hrs | Single session |
| Prior Suria Kod study needed | |||
| Assessed level exits | — | ||
| Written feedback included | |||
| Price | RM 635 | RM 310 | RM 120 |
What Every Programme Shares
Privacy & Data Handling
Learner information is used only to deliver and improve programmes. Not shared with third parties for marketing purposes. See our Privacy Policy for the full details.
Honest Assessment Standards
Assessment criteria are defined before a cohort begins. The standard does not shift based on how the cohort performs. A level is passed when the work meets the stated criteria.
Mentor Availability Posted
The mentor availability board shows exact hours in MYT. If availability changes, the cohort is told in advance. Office hours are not cancelled without notice.
Clear Costs, No Hidden Fees
Prices in Malaysian Ringgit. Refund and withdrawal terms are listed beside the enrolment form.
Six-Level Programme
20 weeks · ~12 hrs/week
RM 635
- Six assessed levels
- Python → Statistics → Models → Neural nets → Deployment → Final project
- Cohort max 20
- 5 evenings/week mentor hours
- Retakes included
Data Engineering Module
7 weeks · 6–8 hrs/week
RM 310
- Pipelines, storage formats, data quality
- Live pipeline for two weeks
- Standalone — no prerequisite
- Written feedback on assessed work
Portfolio Review
90 min · 1-to-1 session
RM 120
- Candid read of your existing projects
- With a working practitioner
- Written notes, priority list after
- No prior enrolment needed
Questions Before You Apply?
Send us a message describing your background and what you are considering. We will tell you plainly whether it is a good fit for where you are now.
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