About Bayu Tech
A commons where learners and mentors meet on equal footing
We started Bayu Tech because we noticed that many AI courses are either too rushed or too isolated. We wanted to build something calmer.
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How Bayu Tech came together
The name Bayu means breeze in Malay — a word that suggests movement without force. That feeling shaped the school from the start. When the founding group first gathered in Kuala Lumpur in early 2022, the conversation kept returning to the same frustration: too many people were dropping out of AI courses not because the material was too hard, but because the pace was disorienting and the feedback was thin.
The three tracks we offer today grew from that conversation. We wanted programmes where a newcomer could begin Python without feeling they were already behind, and where an experienced developer could study deep learning without feeling unsupported. Small cohorts were the answer to both.
Since opening our first cohort in mid-2022, we've worked with learners from across Malaysia and the wider region. Most come with a specific goal in mind — a career shift, a project they want to build, or a gap they want to close. We try to help them reach it without fuss.
Our Mission
What we're trying to do
Bayu Tech exists to give people in Malaysia a structured, honest, and calm way to learn AI development. We are not trying to produce the fastest graduates or the biggest alumni network. We are trying to produce learners who genuinely understand what they've studied and can use it.
Clarity over speed
We pace material weekly so learners have time to absorb, question, and apply before moving forward.
Mentors as equals
Our weekly clinics are conversations, not lectures. Questions of any kind are welcome.
Projects over certificates
Every track ends with work you can show — code, notebooks, models — not just a document to download.
The Team
The people behind the courses
Ahmad Razif
Lead Instructor · ML & Deep Learning
Ahmad designed the Machine Learning and Deep Learning tracks. He has spent eight years working on production ML systems and finds teaching a useful way to stay honest about what actually matters.
Nurul Farhana
Curriculum Lead · Starter & Foundation
Nurul built the AI Starter Course after noticing how often newcomers stalled at the same early points. She focuses on sequencing material so the first few weeks feel clear rather than overwhelming.
Siti Khairani
Learner Support & Operations
Siti handles enrolment, cohort coordination, and the day-to-day questions learners bring between clinics. She keeps things running so instructors can focus on the material.
Standards
How we keep the quality steady
These aren't policies we wrote to display. They're habits that have developed from running small cohorts over several years.
Cohort size limits
Each intake is capped so every learner can get a response to their work within 48 hours. When a cohort fills, we open the next one rather than expand.
Curriculum review cycle
Course material is reviewed after each cohort. Sections where learners frequently stall are rewritten rather than left as-is.
Data privacy practices
Learner information is used only for course administration. We follow Malaysia's Personal Data Protection Act 2010 and do not share details with third parties for marketing.
Open feedback after each track
Every graduate receives a short survey. The responses are read by the instructors directly, not summarised by a third party.
Practical tooling throughout
We use industry-standard tools — Python, Jupyter, common ML libraries — so what you learn here transfers directly to real projects.
Accessible documentation
All course notes, code examples, and recordings are available to enrolled learners throughout the programme, not removed after submission deadlines.
Our Approach
AI education built around the learner, not the platform
Bayu Tech was built with a specific group of people in mind: those in Malaysia who want to develop AI skills seriously but can't commit to a full-time programme or afford to spend months wading through poorly sequenced self-study material. The three tracks — AI Starter, Machine Learning, and Deep Learning Commons — are designed to sit alongside a working week, not replace it.
The commons philosophy running through the school means every learner has access to the same support structures regardless of which track they're on. Weekly clinic hours are not an upsell — they come with the course. Peer channels are not extras — they open when your cohort begins. The distinction between "basic" and "premium" support doesn't exist here.
We draw mentors from people who work with data and models professionally and who can speak to what the material means outside of a course context. When a learner asks whether a concept matters in practice, they get an answer from someone who has actually applied it, not a hedged disclaimer.
For learners based in Kuala Lumpur, the school maintains an office on Jalan Imbi where occasional in-person workshops and study sessions are held. Online participants attend the same clinic hours and access the same materials — geography doesn't affect the quality of what's available.
Curious which track fits where you are now?
Send us a message and we'll have a straightforward conversation about your background and what you're hoping to learn.
Get in Touch