Three programmes. One school. Each built for where you are now.
A 24-week full pathway, an 8-week deployment module and a 3-weekend foundations course. Choose the one that fits your current skill level and schedule.
← Back to HomeHow the programmes are structured
All three programmes at Kelip Akademi share the same operational principles: a fixed cohort start date, a named mentor (or mentor pair), written feedback on every submission within four working days and honest time estimates drawn from tracking real learner hours across previous cohorts.
The programmes differ in scope and prerequisite. The full pathway assumes Python familiarity and some prior data work. The deployment module assumes trained models but no production experience. Weekend Foundations requires neither — it is designed specifically for people assessing whether structured AI learning is right for them.
All exercises run on a learner's own laptop. Each programme description includes a hardware note stating what is needed. No proprietary platform or paid tool subscription is required to complete the work.
Fixed cohort intake dates
Cohorts start together and work through material on the same schedule, enabling peer support within the group.
Named mentor assignment
Every cohort knows who is reviewing their work before the first submission.
Written feedback, four-day turnaround
Specific to the submission, not a rubric score. Tracked and reported honestly.
Real time estimates, real completion data
Available to applicants before they enrol. No optimistic figures.
Full AI Engineering Pathway
24 weeks · RM 625 · Est. 12 hrs/week
A twenty-four week pathway assembling six modules into a single sequence covering data handling, classical machine learning, deep learning, model deployment, monitoring and a supervised capstone. Learners work in cohorts of no more than eighteen with two mentors, and every submission receives written feedback within four working days.
The school publishes its completion rates and its median cohort size so applicants can weigh the commitment realistically. Twelve hours a week is a fair estimate, and the pathway may be paused between modules.
- Data handling and feature engineering
- Classical ML and model evaluation
- Deep learning fundamentals
- Model deployment and monitoring
- Supervised capstone project
PROCESS STEPS
- 01Enrolment and cohort placement
- 02Six sequential modules with regular submissions
- 03Written feedback within four working days each time
- 04Supervised capstone tying modules together
HARDWARE NOTE
Exercises require a laptop with at least 8 GB RAM, a modern CPU (2018 or later) and Python 3.10+. GPU not required for most modules; the deep learning week notes alternatives for CPU-only environments.
HARDWARE NOTE
Exercises require Python 3.10+, Docker Desktop and a free account on one major cloud provider (AWS, GCP or Azure). The module includes setup guidance for all three options.
Model Deployment and Operations
8 weeks · RM 345 · Est. 7 hrs/week
An eight-week module on taking a trained model into service: containerisation, serving frameworks, versioning, monitoring for drift, cost control on cloud infrastructure and the handover documents a working team actually needs. Built around a single project a learner carries through from a notebook to a running endpoint.
Suited to those who have trained models but never shipped one. Seven hours a week including one live evening session per week.
- Containerisation with Docker
- Serving frameworks and API design
- Model versioning strategies
- Monitoring for data and concept drift
- Cloud cost control and handover documentation
PROCESS STEPS
- 01Start from an existing trained model (your own or provided)
- 02Containerise and serve via a REST endpoint
- 03Add monitoring, versioning and cost controls
- 04Produce production-ready handover documentation
Weekend Foundations in Data and Statistics
3 weekends · RM 130 · 6 hrs per weekend
A three-week weekend course covering descriptive statistics, distributions, sampling, correlation and the common ways data work goes wrong before any model is involved. Intended for people considering the longer pathway who would like to test their interest first, and for working professionals who want the vocabulary rather than the full craft.
Six hours across each weekend, with recordings kept available for twelve months. No prior programming is required.
- Descriptive statistics and distributions
- Sampling and uncertainty
- Correlation and causation
- Common ways data work goes wrong
- Recordings available for 12 months
SCHEDULE
- W1Descriptive statistics and data types — Saturday & Sunday, 3 hrs each
- W2Distributions, sampling and inference — Saturday & Sunday, 3 hrs each
- W3Correlation, causation and data quality — Saturday & Sunday, 3 hrs each
HARDWARE NOTE
No programming required. A laptop capable of running a modern browser is sufficient. Spreadsheet examples use LibreOffice Calc, which is freely available.
Choosing the right programme
Use this table to identify which programme matches your current situation. If you are unsure, the Weekend Foundations course is designed specifically to help you decide.
| Feature | Weekend Foundations |
Deployment Module |
Full AI Pathway |
|---|---|---|---|
| Prior programming required | No | Python | Python |
| Prior ML training required | No | Yes | No |
| Duration | 3 weekends | 8 weeks | 24 weeks |
| Live sessions | — | 1/week | Scheduled |
| Written mentor feedback | — | ||
| Cohort peer group | — | ||
| Recordings for 12 months | — | — | |
| Capstone project | — | — | |
| Fee (MYR) | RM 130 | RM 345 | RM 625 |
BEST FOR
Weekend Foundations
Professionals assessing fit, or anyone needing statistical vocabulary without a full course commitment.
BEST FOR
Deployment Module
Data scientists or ML engineers who can train models but have not yet shipped one to production.
BEST FOR
Full AI Pathway
Engineers or analysts who want a complete, structured path from data handling through to a supervised capstone.
Operational standards across all programmes
Privacy and data handling
Learner data is held only for programme operation and administration. No information is shared with advertising or marketing services. Full details in the Privacy Policy.
Feedback turnaround tracking
The four-working-day feedback commitment is tracked for every submission. The actual average is available to prospective learners who ask before enrolment.
Open tools, no locked platforms
All tools used in Kelip Akademi programmes are freely available. No proprietary environment, paid subscription or platform lock-in is required.
Cohort size limit
No cohort in any programme exceeds eighteen learners. This is a policy, not a target. When a cohort fills, the next intake date is offered.
Published programme data
Completion rates, median cohort size and average feedback turnaround are tracked for each programme and available to applicants before they decide to enrol.
Response within one working day
Enquiries through the contact form receive a response within one working day. No automated responses substituting for a real reply.
Programme fees in Malaysian Ringgit
MOD-03
Weekend Foundations
RM 130
3 weekends · 6 hrs/weekend
- Statistics and data foundations
- Weekend schedule only
- Recordings for 12 months
- No programming required
MOD-02
Deployment Module
RM 345
8 weeks · 7 hrs/week
- Containerisation and serving
- Drift monitoring and cost control
- 1 live session per week
- Written feedback per submission
MOD-01 · FULL PATHWAY
AI Engineering Pathway
RM 625
24 weeks · 12 hrs/week
- Six-module full sequence
- Supervised capstone project
- Two named mentors
- Pauseable between modules
- Written feedback per submission
Not sure which programme is right? Ask directly.
Describe where you are now and what you are trying to do. Someone from the school will respond within one working day with a straightforward recommendation.
Send a Message