TinyML Training in Bangkok

This instructor-led, live training held in Bangkok (available online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers looking to apply TinyML techniques to AI-powered applications on energy-saving hardware.

By the conclusion of this program, participants will possess the ability to:

• Comprehend the basics of TinyML and edge AI.
• Deploy compact AI models on microcontrollers.
• Optimize AI inference for reduced power consumption.
• Integrate TinyML with practical IoT use cases.

This instructor-led training in Bangkok empowers advanced professionals to design, optimize, and deploy comprehensive TinyML pipelines. Through practical labs, participants will master data collection, low-power model training, and real-world application testing.

This instructor-led course in Bangkok empowers advanced professionals to protect TinyML pipelines on edge devices. You will gain the skills to apply privacy-preserving methods, reinforce models against adversarial threats, and implement secure data handling best practices in resource-constrained environments.

This instructor-led, live training in Bangkok guides advanced professionals through the integration of TinyML into autonomous robotics. Participants will master the design of optimized models, the implementation of on-device perception pipelines, and the deployment of lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour instructor-led course in Bangkok enables intermediate professionals to implement TinyML for smart agriculture. Discover how to build lightweight models, integrate edge AI with IoT, and optimize systems for precision irrigation and pest detection in a hands-on lab setting.

This live, instructor-led training in Bangkok focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will design models for real-time health data, optimize for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.

This live, instructor-led training in Bangkok empowers advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, implement low-latency inference pipelines, and benchmark performance against memory and energy limits.

This live, instructor-led training in Bangkok (delivered online or onsite) is tailored for intermediate-level IoT developers, embedded engineers, and AI practitioners aiming to deploy TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this program, participants will be capable of:

• Comprehending the fundamentals of TinyML and its IoT applications.
• Configuring a TinyML development environment for IoT projects.
• Building and deploying ML models on low-power microcontrollers.
• Executing predictive maintenance and anomaly detection using TinyML.
• Refining TinyML models for optimal power and memory performance.

This interactive, instructor-led session in Bangkok (online or onsite) is designed for mid-level embedded systems engineers and AI developers looking to implement machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completion, participants will be equipped to:

• Grasp the core concepts of TinyML and its advantages for edge AI scenarios.
• Configure a suitable development environment for TinyML initiatives.
• Train, refine, and execute AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Fine-tune AI models to address power efficiency and memory limitations.

This live, instructor-led training in Bangkok (online or onsite) targets beginner-level engineers and data scientists looking to master TinyML fundamentals, investigate its applications, and deploy AI models on microcontrollers.

By the conclusion of this program, learners will be capable of:

• Comprehending the core concepts of TinyML and its industry relevance.
• Implementing lightweight AI models on microcontrollers and edge hardware.
• Tuning machine learning models for energy efficiency.
• Applying TinyML technologies to real-world tasks like gesture recognition, anomaly detection, and audio processing.