// tensorhaus.school
ML Engineering
for Working Practitioners
Structured courses covering the operational and analytical side of machine-learning systems. Built for engineers who already work with data and want to study ML with technical care.
// courses
What We Teach
Three distinct offerings, each designed around a specific knowledge gap that practitioners encounter when moving into machine-learning work.
// 10 weeks · RM 4,580
MLOps Fundamentals
A careful introduction to the operational side of ML systems for software and data engineers. Covers experiment tracking, model packaging, deployment patterns, monitoring, and documentation.
- Containerised lab environments
- Publicly available tooling only
- Weekly recorded lectures + written reflection
- Model behaviour monitoring in production
// 5 weeks · RM 780
Tabular Data Methods
A short course for analysts and engineers working with tabular business data. Studies gradient-boosted trees, cross-validation, feature engineering, and feature-importance interpretation.
- Notebook-led, practical approach
- Careful cross-validation methods
- Weekly homework assignments
- Feature engineering for business data
// weekly · RM 220/month
Friday Code Reading Hour
A weekly one-hour open session where a senior instructor reads a small piece of public ML code on screen and discusses its structure and reasoning. Unhurried, with live chat questions.
- Live session with chat questions
- Public ML code, annotated in real time
- No assessment — purely educational
- Keeps reading practice steady
// why tensorhaus
What Makes These Courses Different
Practitioner-First Content
Courses are written for people who already write code and query data. Prerequisites are stated plainly; there is no false lowering of the bar.
Open Tooling Throughout
Every lab uses publicly available tools. Nothing you learn here is locked behind a proprietary platform or a vendor subscription.
Honest About Scope
We describe exactly what each course covers and what it does not. Learning outcomes are framed around knowledge and skill, not employment claims.
Structured Pace
Weekly sessions and small labs keep progress steady without demanding you quit your current work. The schedule fits around a full-time role.
Code You Can Read
The Friday session trains the habit of reading other people's code carefully — a skill that matters more in ML engineering than most curricula acknowledge.
Based in Kuala Lumpur
Tensorhaus operates from Kuala Lumpur. Content and pricing are oriented toward the Malaysian engineering market and Southeast Asian practitioners.
// start learning
Ready to study the operational side of ML?
Send us a short note describing your current role and which course interests you. We will reply with enrolment details and any prerequisite questions.
// faq
Common Questions
What level of experience do I need for the MLOps course?
You should be comfortable writing Python and understand what a CI/CD pipeline does in general terms. You do not need prior ML knowledge — the course starts from the assumption that you know how to build and deploy software, and adds the ML-specific operational layer on top of that.
Are the sessions live or pre-recorded?
The MLOps and Tabular Data courses deliver weekly recorded lectures that you can watch on your own schedule. Lab work and reflections follow each lecture at a pace you control. The Friday Code Reading Hour is a live session; recordings are made available to subscribers afterward.
Can I take the Tabular Data course without the MLOps course?
Yes. The two courses are independent. The Tabular Data Methods course focuses on the modelling side; the MLOps Fundamentals course focuses on the deployment and operational side. Many practitioners find it useful to take both over time, but there is no dependency between them.
What software do I need to install for the labs?
A working Docker installation and Python 3.10 or later. All other dependencies are specified in the lab repository. We use only publicly available packages; nothing requires a paid service to run.
How is payment handled?
After you submit your enquiry we will send enrolment instructions and a payment link. We accept online bank transfer (FPX), credit card, and selected e-wallets. Pricing is in Malaysian Ringgit as listed on this page.
Is there any assessment or grading?
The MLOps and Tabular Data courses include small homework assignments and written reflections. These are reviewed and returned with comments; they are not graded on a formal scale. The Friday Code Reading Hour has no assessment component at all — it is purely a reading session.
How is my personal data handled?
Your name and contact details are used only to manage enrolment and communicate about course matters. We do not share your information with third parties for marketing purposes. You can review our full Privacy Policy at any time.
// location
Our Location
Suite 6.3, Plaza Ampang City, Jalan Ampang, 50450 Kuala Lumpur
// contact
Get in Touch
// contact details
+60 3-4257 9083
Call or WhatsApp
Email for enquiries
Suite 6.3, Plaza Ampang City
Jalan Ampang, 50450 Kuala Lumpur
Office Hours
Monday – Friday: 9:00 am – 6:00 pm
Saturday: 10:00 am – 2:00 pm
Sunday & Public Holidays: Closed
// send a message