What learners actually say
These are unedited accounts from people who've been through the programmes — what worked for them, what was challenging, and what they came away with.
Back to Home180+
Programme participants
4.7/5
Average satisfaction
3
Structured programmes
6+
Years combined teaching
Participant experiences
Feedback from learners across the three Synaptik programmes. Dates shown reflect when feedback was submitted.
Razif Azlan
Kuching, Sarawak · AI Foundations
I came in knowing basically nothing about Python. The first two weeks were genuinely challenging but the mentor sessions helped a lot — being able to ask specific questions rather than just rewatching video content made a real difference. By week six I'd built something I was actually proud to show people. The workload is real though, so plan for it.
June 2025
Nurul Liyana
Petaling Jaya, Selangor · ML Engineering
The ML Engineering track was what I needed after years of reading about machine learning but never actually building anything. The code review sessions were the most useful part for me — getting someone to look at my specific implementation and point out what wasn't working properly. Twelve weeks is a reasonable amount of time if you're consistent about putting in the hours each week.
July 2025
Khor Heng Wee
Miri, Sarawak · Advanced AI
The capstone was genuinely demanding — I underestimated how much work the deployment section would involve. The one-to-one sessions with Hafiz were essential; he caught issues in my model pipeline that I'd have missed completely on my own. I came out with a project I can point to when talking about my experience with deep learning. Worth it, but go in with your eyes open about the time commitment.
July 2025
Siti Farahanis
Kuala Lumpur · AI Foundations
I was working full-time through the eight weeks, which was manageable but tight. The live sessions in the evening fit my schedule. The mentor was patient and didn't make me feel behind when I didn't get something immediately. The completion record was something I hadn't expected to feel so meaningful, but finishing the programme and having something to show for it genuinely felt good.
June 2025
Joshua Tan
Penang · ML Engineering
What I valued most was that the exercises used real-ish data, not perfectly clean toy datasets. The messiness of actual data handling was something I hadn't encountered in self-study. The code review sessions were sharp — my mentor picked up a feature engineering issue that would have tanked the model evaluation entirely. The small group size means you don't disappear into the cohort.
May 2025
Ahmad Izzuddin
Kota Kinabalu, Sabah · Advanced AI
The deployment section was something I hadn't seen properly covered in any other programme I'd looked at — most stop at training the model. Going through versioning, monitoring, and actually getting something into a realistic deployment environment was new territory. There were parts of the capstone I found harder than expected, but that's probably how it should be at this level.
July 2025
How participants moved forward
Three detailed accounts of where learners started, how they worked through each programme, and what they came out with.
Challenge
From admin to data work — no code background
Fauziah worked in administrative roles and had no programming background. She wanted to move into data-related work but found self-paced tutorials difficult to stick with — she kept starting courses and dropping off after the first few modules without clear direction or feedback.
Journey
AI Foundations Programme · 8 weeks
Fauziah joined the AI Foundations cohort and worked through the programme while continuing her job. The live session format gave her a weekly structure that self-paced content hadn't. She found the mentor feedback on her exercises helped her understand where her reasoning was going wrong, rather than just whether her code was producing the right output.
Result
Portfolio project completed, continued to ML track
She completed the starter portfolio project — a small classification model on a public dataset — and used that as the basis for applying to a junior data analyst position. She also enrolled in the ML Engineering track the following cohort to build further depth.
"Having something real to show rather than just a course name made the application conversation feel completely different."
Challenge
Software engineer wanting to move into ML roles
Darren had been working as a backend developer for four years. He had solid Python skills but had never worked on ML projects professionally. He wanted structured experience with real model-building workflows to make the transition credible rather than just theoretical.
Journey
ML Engineering Track · 12 weeks
As someone comfortable with code, Darren found the technical exercises came quickly at first. The deeper challenge came in feature engineering and model evaluation — areas where engineering instincts don't automatically transfer. Code reviews caught decisions he'd made for engineering reasons that weren't appropriate for ML workflows.
Result
Two applied portfolio pieces, clearer transition path
Darren finished with two applied portfolio pieces and a much more specific sense of which ML engineering areas he wanted to focus on. He subsequently enrolled in the Advanced track to work on deployment specifically, which he saw as the gap between his current role and the work he wanted to do.
"The code reviews were the most valuable part. They flagged things that a static tutorial couldn't possibly catch."
Challenge
Research background, no experience shipping models
Priya had a postgraduate background in statistics and some exposure to ML in academic settings. Her gap was practical — she could read and understand ML papers but had no experience with the engineering side of getting models from notebooks into production environments.
Journey
Advanced AI Development & Deployment · 16 weeks
Given her background, Priya enrolled directly in the Advanced track. The early deep learning weeks were familiar ground, but the deployment and versioning sections were entirely new. One-to-one mentoring sessions with Hafiz helped her connect her statistical understanding to practical engineering decisions.
Result
Capstone delivered, engineering fundamentals established
Her capstone — a fine-tuned text classification model with a basic monitoring setup — was the first complete ML project she'd taken from training to a deployed state. The career-preparation session helped her frame her academic background and practical capstone work for industry conversations.
"I finally know what I don't know, which is honestly more useful than thinking I had it covered."
Contact the Synaptik team
Address
9, Jalan Tabuan, 93100 Kuching, Sarawak
Hours
Mon–Fri: 9am–6pm
Sat: 10am–2pm
Professional context
MSC Malaysia Technology Partner
Recognised under Malaysia's digital economy and technology development programme
SSM Registered
Formally registered with Suruhanjaya Syarikat Malaysia as an operating business
Sarawak Digital Economy Association
Active member of the regional tech and digital economy community
Thinking about joining a cohort?
We're happy to answer questions before you decide. Send us a message and we'll give you a clear, straight answer about whether a programme suits your background.
Book a Conversation