// solutions
Three courses.
One clear purpose.
The Tensorhaus catalogue addresses the operational, analytical, and reading layers of ML engineering practice, each as a distinct offering suited to different learning needs.
Back to Home// methodology
How Tensorhaus Designs Courses
Each Tensorhaus course begins with a problem that comes up repeatedly in engineering work. For the MLOps course, the problem is this: a data engineer or software engineer who understands pipelines and version control but has not studied the specific operational challenges of ML systems — experiment tracking, model artefact management, monitoring for data and concept drift, documentation patterns for reproducibility. The course addresses that problem in ten weeks at a pace that fits a full-time schedule.
The Tabular Data course addresses a different problem: an analyst or data engineer who works with tabular business data and wants to understand the modelling methods that perform well on this type of data, studied with technical discipline rather than as a sales pitch for a particular library.
The Friday session addresses something neither course covers: the habit of reading code carefully. This is an engineering skill that is practised but rarely taught. The session provides a weekly structure for it.
// course 01 · 10 weeks
MLOps Fundamentals
RM 4,580
A ten-week course for software engineers and data engineers who would like a careful introduction to the operational side of machine-learning systems. The course covers experiment tracking, model packaging, simple deployment patterns, monitoring of model behaviour in production, and the documentation habits that keep a system maintainable. Each week consists of a recorded lecture, a small lab in a containerised environment, and a written reflection. The course is delivered with publicly available tooling.
// key topics
- Experiment tracking with MLflow — logging parameters, metrics, and artefacts
- Model packaging with Docker and standard serialisation formats
- Simple HTTP inference endpoints — structure and reliability patterns
- Monitoring for data drift and model performance degradation
- Documentation that keeps a model system maintainable over time
// weekly structure
Recorded lecture covering the week's topic — watch on your own schedule
Lab notebook in a containerised environment — apply the lecture material to a concrete problem
Written reflection — articulate what you understood and where uncertainty remains
Prerequisites: Python, Docker basics, familiarity with CI/CD concepts. Prior ML knowledge not required.
Enquire About This Course// course 02 · 5 weeks
Tabular Data Methods
RM 780
A five-week short course for learners who work with tabular business data and would like to study the methods that perform well on this kind of problem with technical care. Topics include gradient-boosted trees, careful cross-validation, feature engineering for tabular data, and the reading of feature-importance results. Each week consists of a recorded lecture, a notebook, and a small homework. The course is practical and notebook-led.
// key topics
- Gradient-boosted trees — LightGBM and XGBoost, structure and tuning
- Cross-validation done carefully — stratification, leakage prevention, evaluation
- Feature engineering specific to tabular business data
- Reading feature-importance and SHAP outputs correctly
- Practical notebook organisation for reproducible analysis
// weekly structure
Recorded lecture introducing the week's method with worked examples
Notebook — apply the method to a provided dataset, modify and experiment
Small homework — a variation problem that tests understanding of the key idea
Prerequisites: Python, pandas, basic statistical concepts (mean, variance, distributions). Prior ML experience helpful but not required.
Enquire About This Course
// session · weekly
Friday Code Reading Hour
RM 220 / month
A weekly one-hour open session in which a senior instructor reads a small piece of public machine-learning code on screen and discusses how it is structured and why. The session is unhurried and educational. Attendees can ask questions in chat. The session is provided as a quiet space for working engineers to keep their reading practice steady; it is not a course or assessment.
// what happens in a session
- Instructor selects a short, meaningful piece of public ML code
- Code is read aloud and annotated on screen, section by section
- Design choices and trade-offs are discussed as they appear
- Attendees post questions in chat; instructor addresses them during reading
- Recording provided to subscribers within 24 hours
No prerequisites beyond reading Python. No assessment, no homework. This session is a reading practice, not a course.
Enquire About This Session// decision guide
Choosing the Right Course
The three offerings address different needs. Use this table to match your situation to what each course provides.
| Feature | MLOps Fundamentals | Tabular Data Methods | Friday Code Reading |
|---|---|---|---|
| Duration | 10 weeks | 5 weeks | Ongoing monthly |
| Price (RM) | RM 4,580 | RM 780 | RM 220/month |
| Best for | Engineers moving into ML ops roles | Analysts adding ML methods | Any practitioner building a reading habit |
| Lectures included | Yes | Yes | — |
| Lab environment | Docker-based | Notebooks | — |
| Instructor feedback on submissions | Yes | Yes | — |
| Live session | — | — | Weekly Friday |
| Session recording provided | — | — | Yes, within 24h |
| HRDF Claimable | Yes | Yes | — |
// pricing
Course Fees
// 10 weeks
MLOps Fundamentals
RM 4,580
- 10 recorded lectures
- 10 containerised labs
- Written reflections with feedback
- Async support channel
- HRDF claimable
// 5 weeks · best entry point
Tabular Data Methods
RM 780
- 5 recorded lectures
- 5 notebook labs
- Homework with feedback
- Async support channel
- HRDF claimable
// monthly subscription
Friday Code Reading
RM 220/month
- 4–5 live sessions per month
- Session recordings within 24h
- Chat Q&A during session
- No assessment or homework
- Cancel any time
Prices quoted in Malaysian Ringgit (RM) and inclusive of applicable taxes. Payment via FPX, credit card, or e-wallet. HRDF claims available for eligible companies on the two full courses.
// standards
Technical Standards Across All Courses
Access Control
Course materials are accessible only to enrolled participants via individual credentials. Access is tied to the enrolment period and managed securely.
Environment Testing
Lab environments are tested on clean systems before each cohort begins. Dependency versions are pinned and verified to prevent mid-course setup failures.
Data Privacy
Participant data is held only for enrolment and course delivery purposes. We do not share contact details with third parties or use them for unrelated communications.
Annual Revision
Materials are reviewed and revised annually. Sections referencing deprecated tooling or outdated practice are rewritten, not left in place with warnings attached.
Support Response
Async support channels are checked on weekday mornings. Questions that cannot be resolved in writing are addressed in the weekly office-hours slot during the course period.
Receipts and Documentation
Official receipts are issued for all payments. HRDF claim documentation is provided on request for eligible companies enrolling participants in the full courses.
// enrol
Ready to start?
Send an enquiry mentioning which course interests you and your current technical role. We will reply with enrolment details and any prerequisite notes.
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