TinyML: Running AI on Ultra-Low-Power Edge Devices Training Course

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen to explore how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimise AI workloads for efficient performance on edge devices.

This forward-looking course examines how 6G wireless technologies integrate with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

Designed for intermediate-level IT architects, this instructor-led live training (available online or onsite) helps participants understand and design next-generation distributed architectures by leveraging the synergy between 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G.
• Evaluate business and operational models enabled by the convergence of 6G and edge technologies.

Course Format

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Customization Options

• For customized training options for this course, please contact us to arrange.

This instructor-led, live training in Malaysia (online or on-site) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

This instructor-led live training in Malaysia (online or onsite) is designed for intermediate-level developers, data scientists, and tech enthusiasts looking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

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) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

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.

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.

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.

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.

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.

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.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices directly in the field.

This instructor-led live training (available online or onsite) is tailored for intermediate-level professionals aiming to apply TinyML techniques to smart agriculture solutions, thereby enhancing automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Construct and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems to enable automated crop monitoring.
• Utilise specialized tools to train and optimise lightweight models.
• Establish workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

• Guided presentations coupled with applied technical discussions.
• Hands-on practice using real-world datasets and hardware devices.
• Practical experimentation within a supported laboratory environment.

Course Customization Options

• For tailored training aligned with specific agricultural systems, please contact us to customize the program.