TinyML with Raspberry Pi and Arduino Training Course

This instructor-led course in Malaysia empowers advanced professionals to architect, optimize, and launch complete TinyML pipelines. Learners will acquire the skills to gather data, train energy-efficient models, and test real-world applications through hands-on labs.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Establish a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Utilise TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimize AI models to meet power efficiency and memory constraints.

Delivered in Malaysia, this live instructor-led training empowers advanced practitioners to refine TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning techniques, construct low-latency inference pipelines, and evaluate performance relative to memory and energy constraints.

This instructor-led training in Malaysia empowers advanced professionals to secure TinyML pipelines on edge devices. You will gain the skills to implement privacy-preserving techniques, reinforce models against adversarial attacks, and apply best practices for secure data handling in resource-constrained settings.

This instructor-led live training in Malaysia (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and appreciate its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimise and fine-tune machine learning models for minimal power usage.
• Apply TinyML to real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

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

This instructor-led, live training in Malaysia (online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers who wish to apply TinyML techniques for AI-driven applications on energy-efficient hardware.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This live, instructor-led training in Malaysia focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimization for low-power wearables, and ensuring clinical reliability, supported by hands-on lab exercises.

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

This 21-hour instructor-led course in Malaysia empowers intermediate professionals to deploy TinyML for smart agriculture. Participants will learn to build lightweight models, integrate edge AI with IoT, and optimize systems for precision irrigation and pest detection within a hands-on lab setting.