LEARNER EXPERIENCES
What Learners Say About Veyl
Feedback from individuals and teams who have worked through our GPU architecture programmes.
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Farhan Zulkifli
ML Engineer · Kuala Lumpur
"I had been working with PyTorch for about two years before this but kept hitting questions I couldn't answer about why certain batch sizes behaved the way they did. The Reading Track laid out the memory hierarchy in a way that actually connected to what I was observing. Module 5 on shared memory was where things clicked for me."
GPU Fundamentals Reading Track · May 2025
Nurul Khairina
Data Scientist · Petaling Jaya
"The Lab Companion is genuinely practical in a way that most 'hands-on' courses aren't. Working through actual spec sheets rather than toy examples made the exercises feel relevant. I would say the workload sizing sessions (8 and 9) are worth the price of the whole companion on their own."
Hands-On Lab Companion · April 2025
Chen Wei Liang
Software Architect · Cyberjaya
"We put six people from our infrastructure team through the Team Knowledge Programme in March. The pre-session context call was useful — the facilitator clearly read what we sent and adjusted the workshop examples to match our deployment environment. The reference handbook is still sitting on the team's shared drive and gets opened regularly."
Team Knowledge Programme · March 2025
Siti Amirah
Final-year CS student · Shah Alam
"As someone finishing my degree, I was looking for something that would help me talk credibly about hardware in interviews for ML roles. The Reading Track covered exactly the kind of foundational questions that come up. The glossary at the end of each module is genuinely useful — not just a list of definitions but small explanations of why each term matters."
GPU Fundamentals Reading Track · May 2025
Rajesh Nair
MLOps Engineer · Kuala Lumpur
"I work primarily on the operations side of ML deployments and found the Lab Companion covered the dashboard interpretation exercises extremely well. The section on SM occupancy and what it means for throughput was more actionable than anything I found in the official documentation. One suggestion: more exercises around multi-GPU setups would be a welcome addition."
Hands-On Lab Companion · April 2025
Lim Hui Shan
Engineering Manager · Selangor
"We are a team of twelve and the hardware literacy gap was creating friction in planning conversations — some people had strong intuitions about GPU behaviour while others were working from very general assumptions. The Team Knowledge Programme brought everyone to a shared vocabulary over four weeks. The fact that it was HRDC-claimable made the budget conversation simple."
Team Knowledge Programme · February 2025
Learning Journeys
How three learners moved from initial gaps to applied hardware understanding.
ML Engineer — From unexplained slowdowns to confident workload reasoning
STARTING POINT
Two years of ML experience with PyTorch. Understood model architecture well but kept encountering GPU-related slowdowns without knowing where to look for the cause. Had read documentation but found it hard to connect spec numbers to observed behaviour.
PROGRAMME PATH
Completed the GPU Fundamentals Reading Track over five weeks, focusing particularly on memory hierarchy and the throughput modules. Followed up with two Lab Companion exercises on his own after the track to check his understanding against practical scenarios.
OUTCOME
Reported being able to diagnose a memory bandwidth bottleneck in a production training job within two weeks of finishing the track — an issue that had been attributed to a dataset problem for several months. Now reads GPU profiler outputs with confidence.
Engineering Team — Building shared vocabulary across twelve engineers
STARTING POINT
A twelve-person engineering team split between those with hardware intuition and those working from software abstractions only. Planning conversations around GPU provisioning often stalled or produced decisions that senior engineers later revised without explanation.
PROGRAMME PATH
Enrolled in the Team Knowledge Programme. Facilitator conducted a pre-programme context session with the engineering manager and two senior engineers. Workshop scenarios were drawn from the team's actual provisioning decisions and common friction points.
OUTCOME
GPU provisioning discussions in sprint planning now conclude in under thirty minutes where they previously ran long without resolution. The reference handbook is actively used. Three team members requested individual Lab Companions for deeper study after the programme ended.
Computer Science Student — Preparing for ML hardware questions in technical interviews
STARTING POINT
Final-year CS student with project experience in ML but no formal hardware study. Aware that ML infrastructure roles often ask hardware-related questions and wanted to build defensible knowledge before internship and graduate applications.
PROGRAMME PATH
Worked through the GPU Fundamentals Reading Track at two modules per week alongside university coursework, taking advantage of the self-paced format. Used the knowledge checks to identify where to reread before progressing.
OUTCOME
Received and accepted an ML infrastructure internship offer the month after completing the track. Credited the programme with giving her concrete, structured answers to hardware questions that had previously made her uncertain in technical conversations.
Get in Touch
Phone
+60 3-2148 7693Address
Jalan Ampang 128
50450 KL, MY
Hours (MYT)
Mon–Fri 9am–6pm
Sat 10am–2pm
In Numbers
120+
Learners enrolled
14
Team programmes delivered
4.7
Average satisfaction (out of 5)
MY
Based in Malaysia — MYT hours
MDec Digital Education Listing
Recognised provider under the Malaysia Digital Economy Corporation continuing education directory.
HRDC Claimable Programmes
Team Knowledge Programme eligible for Human Resources Development Corporation training claims for Malaysian employers.
Defined Review Schedule
All content reviewed on a documented cycle to stay current with GPU architecture changes.
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