Three Tracks,
One Connected Stack
Each track is a self-contained module in applied AI work. Together they cover the full path from raw data to a running system — choose the layer you need, or work through the sequence.
Back to HomeHow the Tracks Are Structured
Every Tensora track begins with a task rather than a lecture. The task is grounded in a real system — a dataset to process, a model to train, a pipeline to design. Technical concepts enter when the work requires them, which means learners encounter them at the moment they are most legible.
Modules are time-boxed to work within the constraints of a full-time schedule. There is no assumption of unlimited availability — the scope for each week is defined clearly, and the expected time investment is realistic.
Submissions are reviewed by instructors who are familiar with the rest of the cohort's work. This means feedback is calibrated — it addresses where your particular approach diverges, not just whether the output runs.
Materials from each track remain accessible after the cohort concludes, so learners can return to them when the same concepts come up in their own work.
Applied Machine Learning Track
Project-led learning where students build, evaluate and document real models on practical datasets. Designed for learners ready to move from theory into hands-on work. The track covers the decisions that come up before training — feature selection, dataset preparation — and after it: evaluation, documentation, and knowing when a result is useful.
What You Build and Learn
- Select and prepare features from real-world tabular datasets
- Train and compare classification and regression models
- Evaluate model performance using appropriate metrics for the task
- Document findings in reproducible notebooks with clear rationale
- Work with scikit-learn, pandas, and standard evaluation tooling
Module Sequence
- 01Dataset exploration and problem framing
- 02Feature engineering and preprocessing
- 03Model training, selection, and tuning
- 04Evaluation, interpretation, and final documentation
Track fee
฿3,850
Data Engineering Foundations
Coursework on pipelines, storage, and preparing data so models can be trained reliably. The track addresses the infrastructure that sits beneath applied ML — the work that rarely appears in tutorials but determines whether training runs produce anything useful.
What You Build and Learn
- Design and implement data ingestion pipelines from multiple source types
- Work with structured storage systems and schema design
- Build batch and streaming data flow patterns
- Validate, clean, and prepare datasets for downstream training tasks
- Document pipeline decisions with reproducibility in mind
Module Sequence
- 01Data sources, ingestion patterns, and schema decisions
- 02Storage design and query patterns
- 03Transformation, validation, and quality checks
- 04Pipeline documentation and handoff to model training
Track fee
฿6,300
MLOps & Deployment Track
Coursework on packaging, serving, and monitoring models in production-style settings. For learners who want to ship and maintain, not only train. The track moves through the decisions that come after training is done — how a model gets wrapped, served, observed, and kept working over time.
What You Build and Learn
- Package trained models into reproducible, versioned artefacts
- Build serving infrastructure for batch and real-time inference
- Set up monitoring, logging, and drift detection pipelines
- Reproduce production-style deployment workflows end to end
- Manage configuration and environment dependencies for reliability
Module Sequence
- 01Packaging and versioning trained models
- 02Building and testing serving infrastructure
- 03Observability: monitoring, logging, and drift handling
- 04Full deployment walkthrough and production-readiness review
Track fee
฿11,550
Track Fees
All prices in Thai Baht. Each track is a one-time fee covering the full cohort, materials, and instructor feedback.
Applied ML
Best for: Learners with Python experience wanting to build and evaluate real models.
฿3,850
- 6–8 week cohort
- 4 project modules
- Instructor feedback on submissions
- Ongoing access to materials
Data Engineering
Best for: Learners who want to build the infrastructure that makes ML work reliably.
฿6,300
- 6–8 week cohort
- 4 pipeline-focused modules
- Instructor feedback on submissions
- Ongoing access to materials
MLOps & Deployment
Best for: Learners who have trained models and want to take them into production.
฿11,550
- 6–8 week cohort
- 4 deployment-focused modules
- Instructor feedback on submissions
- Ongoing access to materials
Which Track Fits Your Situation
| What you want to do | Applied ML | Data Eng. | MLOps |
|---|---|---|---|
| Build and evaluate predictive models | |||
| Design data pipelines and storage | |||
| Package and serve models in production | |||
| Understand feature engineering | |||
| Set up monitoring and observability | |||
| Follow the full stack end to end |
Not sure which fits your situation? Send us a message and describe your background — we can help you work it out.
Standards Across All Tracks
Data Privacy
Datasets used in coursework are publicly available or synthetic. No learner or personal data is used in project exercises. Consistent with PDPA requirements.
Reproducibility
All project outputs are expected to be reproducible. Submission requirements include environment files, clear dependency management, and documented steps.
Feedback Turnaround
Instructor feedback on submissions is delivered within the same module window. Learners are not left waiting across multiple weeks for a response on their work.
Curriculum Maintenance
Track content is reviewed and updated after each cohort. Tooling versions, dataset choices, and approach guidance are kept current with the field.
Honest Scope
Each track describes clearly what is and is not covered. We do not inflate outcomes or describe tracks as paths to specific job roles. The value is the understanding built, not a title.
Support Availability
Questions during a cohort can be raised through the group or directly to the instructor. Support is not AI-generated or automated — responses come from the people running the track.
Not Sure Which Track to Start With?
Send a note with a bit about your current background and what you are trying to learn next. We will respond with an honest assessment of which track makes sense, and whether now is the right time.
Get in Touch