TinyML for IoT Applications Training Course

TinyML involves the deployment of optimised machine learning models on edge devices with limited resources.

This instructor-led live training (available online or onsite) is designed for advanced technical professionals aiming to design, optimise, and deploy comprehensive TinyML pipelines.

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

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Transform models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications on real hardware.

Course Format

• Instructor-led lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customisation Options

• To tailor the training with specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements.

This instructor-led, live training in Malaysia (online or onsite) targets intermediate-level IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals looking to utilise Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Grasp the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led live training in Malaysia (online or onsite) is tailored for product managers and developers who intend to utilise Edge Computing to decentralise data management, enhancing performance by leveraging smart devices at the network source.

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

• Understand the core concepts and advantages of Edge Computing.
• Identify use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to optimise IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

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.

In this instructor-led, live training in Malaysia, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and how they integrate to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

TinyML involves the deployment of machine learning models onto hardware with severely limited resources.

This instructor-led live training, available either online or in-person, targets advanced practitioners aiming to optimise TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be capable of:

• Utilising quantization, pruning, and compression techniques to shrink model sizes while maintaining accuracy.
• Benchmarking TinyML models to assess latency, memory usage, and energy efficiency.
• Deploying optimised inference pipelines on microcontrollers and edge devices.
• Assessing the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led sessions complemented by technical demonstrations.
• Practical exercises in optimisation alongside comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab setting.

Customisation Options

• For bespoke training tailored to specific hardware platforms or internal workflows, please contact us to customise the programme.

TinyML represents a methodology for deploying machine learning models onto low-power devices with limited resources, typically operating at the network edge.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be equipped to:

• Recognize security vulnerabilities specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply industry best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML platforms.

Customization Options

• Organizations may request a customized version of this training to meet their specific security and compliance requirements.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization for your IoT solutions.

In this instructor-led live training, participants will learn how to integrate ThingsBoard into their IoT solutions.

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

• Install and configure ThingsBoard
• Understand the fundamentals of ThingsBoard features and architecture
• Build IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for telemetry device data routing
• Integrate ThingsBoard with Apache Spark for data aggregation from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the course

• Part lecture, part discussion, exercises, and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

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.

TinyML provides a framework for deploying machine learning models on low-power microcontrollers and embedded platforms within robotics and autonomous systems.

This instructor-led training session, available both online and onsite, targets advanced professionals seeking to integrate perception and decision-making capabilities powered by TinyML into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Course Format

• Technical lectures paired with interactive discussions.
• Hands-on labs focused on embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Customization Options

• Customization for organization-specific robotics environments can be arranged upon request.

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.

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customization Options

• For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program.

TinyML represents a machine learning approach specifically optimized for small devices with limited resources.

This instructor-led live training, available online or onsite, is designed for beginner to intermediate learners looking to create functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

After completing this training, participants will be able to:

• Gather and prepare data for TinyML projects.
• Train and optimize small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop complete embedded AI prototypes.

Course Format

• Instructor-led presentations and guided discussions.
• Practical exercises and hands-on experimentation.
• Live lab project work on real hardware.

Course Customization Options

• For tailored training aligned with your specific hardware or use case, please contact us to arrange.