TinyML for IoT Applications Training Course

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

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

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

TinyML involves deploying highly optimized machine learning models onto edge devices with limited resources.

This guided, live training session—available online or in-person—is designed for experienced technical professionals looking to design, optimize, and implement complete TinyML workflows.

Upon completion of this course, participants will gain the ability to:

• Gather, preprocess, and manage datasets specifically for TinyML use cases.
• Train and refine models to operate efficiently on low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications across actual hardware setups.

Course Format

• Instructor-led lectures paired with technical discussions.
• Practical laboratory sessions and iterative experimentation.
• Hands-on deployment exercises using microcontroller-based platforms.

Customization Options

• To tailor the training to your specific toolchains, hardware boards, or internal workflows, please reach out to us for arrangements.

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

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

• Comprehend the foundational concepts of IoT and edge computing and their role in digital transformation.
• Identify specific use cases for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Apply edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led live training, located in Vietnam (offered online or onsite), is designed for intermediate-level developers, system architects, and industry professionals seeking to leverage Edge AI to augment IoT applications with sophisticated data processing and analytical capabilities.

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

• Comprehend the core principles of Edge AI and its integration into IoT systems.
• Prepare and configure Edge AI environments suitable for IoT device operation.
• Create and install AI models on edge devices to support IoT applications.
• Deploy real-time data processing mechanisms and decision-making algorithms in IoT systems.
• Connect Edge AI solutions with various IoT protocols and platform ecosystems.
• Address ethical concerns and adopt best practices for Edge AI deployment in IoT.

This instructor-led live training in Vietnam (online or onsite) is designed for product managers and developers who wish to utilize Edge Computing to decentralize data management, achieving faster performance by leveraging smart devices located on the source network.

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 effectively applied.
• Design and build Edge Computing solutions aimed at faster data processing and reduced operational costs.

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

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

• Gain a solid understanding of the principles and advantages of Federated Learning in IoT and edge computing contexts.
• Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities within edge computing environments.
• Tackle challenges associated with data privacy and network limitations in IoT systems.

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

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

• Understand the fundamental principles of TinyML and its benefits for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Apply TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models for power efficiency and memory constraints.

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

This instructor-led live training, available both online and onsite, is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployments on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to minimize model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical exercises in model optimization and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab setting.

Customization Options

• For specialized training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who want to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led, live training in Vietnam (available online or onsite) targets beginner-level engineers and data scientists seeking to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications 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, which are commonly used in robotics and autonomous systems.

This instructor-led live training (available online or onsite) is designed for advanced professionals who want to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be able to:

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

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in Vietnam (online or onsite) is designed for 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:

• Grasp 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 professionals looking to implement TinyML solutions for health monitoring and diagnostic applications.

Upon completing this training, participants will be equipped 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 to function efficiently on low-power and memory-limited wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures augmented with live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks conducted in a guided lab environment.

Customization Options

• For customized training tailored to specific healthcare devices or regulatory workflows, please contact us to adapt the program.

TinyML is a machine learning approach optimized for small, resource-constrained devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, attendees will gain the skills to:

• Collect 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 end-to-end embedded AI prototypes.

Format of the Course

• 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 seeking to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems to enable automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Develop 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 devices.
• Practical experimentation within a supported lab environment.

Course Customization Options

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