Building Practical AI Knowledge
in Bangkok
Algonest was set up to fill a gap between theory-heavy curricula and the actual skills developers need when working with machine learning in practice.
Back to HomeHow Algonest Came About
Algonest started in 2021 when a group of developers and engineers working in Bangkok's technology sector noticed a pattern: many graduates and self-taught programmers understood the concepts behind machine learning but had little experience turning that understanding into working, deployable systems.
The original programme was a single workshop on model deployment, run on evenings over four weeks. The format worked — participants left with something they could use immediately. That convinced us to expand into computer vision and language modelling, each structured around the same principle: you spend more time writing code and debugging pipelines than reading slides.
Today, Algonest runs three course tracks from our location in Pom Prap Sattru Phai, with a small, experienced instructional team. We keep cohorts small and sessions concrete. We're not trying to be the biggest — we're trying to be the one that actually moves people forward.
Mission
To give developers and technical practitioners working in Thailand a structured, honest path into applied AI — without overpromising what education alone can deliver.
Approach
Project work from the first session. Real tools, not demos. Small groups with genuine instruction time. Each track is designed to produce something you can point to when it's done.
Values
Honesty about what courses cover and what they don't. Respect for participants' existing knowledge. Straightforward pricing. No pressure tactics or vague promises.
The People Behind the Courses
Pattanapong Nantakorn
Lead Instructor — Computer Vision
Seven years working on image processing and embedded vision systems before moving into education. Leads the Computer Vision track and curriculum design.
Kanya Wiriyatham
Instructor — NLP & Language Models
Has spent most of her career working with text data in commercial settings, from search systems to document classification. Developed the NLP Workshop content from the ground up.
Somchai Suwan
Instructor — MLOps & Deployment
Background in DevOps and platform engineering before specialising in ML infrastructure. Teaches the MLOps track with a focus on production-readiness and monitoring.
How We Maintain Course Quality
Quality in technical education shows up in specifics, not marketing language. Here's what we actually do.
Practical Assessments Only
We don't use multiple-choice tests. Progress is evaluated through project submissions — working code, documented pipelines, and instructor review.
Curriculum Updated Twice Yearly
AI tooling changes quickly. Course materials are reviewed and updated before each intake to reflect current libraries, deployment practices, and industry patterns.
Cohort Size Cap
Sessions are kept to a maximum of 12 participants. This is a deliberate choice — larger groups make individual feedback harder to deliver properly.
Data Privacy in Practice
Personal data collected during enrolment is handled according to Thailand's PDPA framework. We collect what's needed and nothing more.
Feedback After Every Session
Short feedback forms go out after each session. Responses are read by the instructor and used to adjust the pacing and approach for the remaining sessions in a cohort.
Code Review Included
Project submissions receive written code review from the course instructor. Feedback covers structure, logic, and practical improvements — not just whether the output runs.
Applied AI Education in Thailand's Technical Community
The demand for people who can work with machine learning systems in Thailand has grown steadily over recent years, particularly in sectors like logistics, financial technology, and manufacturing. The gap between available positions and candidates with relevant hands-on experience remains wide.
Algonest sits in that gap. We work with developers, data analysts, and engineers who understand software and want to extend that knowledge into AI and ML. Our three tracks — Computer Vision Fundamentals, Natural Language Processing Workshop, and MLOps & Deployment — address the most commonly requested skills in practical ML work.
What makes our approach different from most is the structure: sessions are short enough to fit alongside work commitments, and every module ends with something functional. We cover tools that appear in real ML workflows — PyTorch for modelling, Hugging Face for language model work, FastAPI and Docker for serving, and standard monitoring approaches for deployed systems.
We operate from Bangkok and work primarily with participants based in Thailand, though remote participation has been available for some cohorts. If you're somewhere outside Bangkok and interested in joining, get in touch and we'll explain what's currently possible.
Interested in joining a cohort?
We'll share current scheduling, prerequisites, and what to expect before your first session. No pressure.
Send an Enquiry