TinyML for IoT Applications Training Course

TinyML is the practice of deploying optimised machine learning models on resource-constrained edge devices.

This instructor-led, live training (delivered online or on-site) is designed for advanced-level technical professionals who aim to design, optimise, and deploy complete TinyML pipelines.

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

• Collect, prepare, and manage datasets for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Course Format

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

Course Customisation Options

• To customise the training with specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for intermediate-level IT professionals and business managers seeking to understand the potential of IoT and edge computing to drive efficiency, enable real-time processing, and foster 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 within the 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 New Zealand (available online or on-site) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Understand 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 New Zealand (online or on-site) is designed for product managers and developers who wish to leverage Edge Computing to decentralise data management, achieving faster performance by utilising smart devices located at the source network.

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

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

This instructor-led, live training in New Zealand (available online or on-site) is designed for intermediate-level professionals seeking to apply Federated Learning to optimise IoT and edge computing solutions.

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

• Understand the core principles and benefits of Federated Learning in IoT and edge computing contexts.
• Implement Federated Learning models on IoT devices to enable decentralised AI processing.
• Reduce latency and enhance real-time decision-making in edge computing environments.
• Tackle challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in New Zealand (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.

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

• Understand the fundamentals of TinyML and its advantages for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimise AI models for power efficiency and memory constraints.

In this instructor-led, live training in New Zealand, participants will explore the various facets of NB-IoT (also known as LTE Cat NB1) as they develop and deploy a sample NB-IoT-based application.

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

• Identify the distinct components of NB-IoT and understand how they integrate to form a cohesive ecosystem.
• Understand and articulate the security features embedded within NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

TinyML is the practice of deploying machine learning models on highly resource-constrained hardware.

This instructor-led, live training (online or on-site) is aimed at advanced-level practitioners who wish to optimise TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantisation, pruning, and compression techniques to reduce model size without sacrificing accuracy.
• Benchmark TinyML models for latency, memory consumption, and energy efficiency.
• Implement optimised inference pipelines on microcontrollers and edge devices.
• Evaluate trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

• Instructor-led presentations supported by technical demonstrations.
• Practical optimisation exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customisation Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customise the program.

TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge.

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

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

• Identify security risks unique 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.

Course Format

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

Course Customisation Options

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

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

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 with 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

Course format

• A blend of lecture, discussion, exercises, and extensive hands-on practice

Note

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

This instructor-led, live training in New Zealand (available online or on-site) is designed for beginner-level engineers and data scientists who wish to understand the fundamentals of TinyML, explore its practical applications, and deploy AI models on microcontrollers.

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

• Grasp the core principles of TinyML and understand its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimise and fine-tune machine learning models for low-power consumption.
• Apply TinyML to real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

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

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

Upon completing this course, participants will be able to:

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

Course Format

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

Course Customisation Options

• For organisation-specific robotics environments, customisation can be arranged upon request.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who want to apply TinyML techniques to build AI-powered 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.
• Optimise AI inference for minimal power consumption.
• Integrate TinyML into real-world IoT applications.

TinyML refers to the integration of machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training (available online or on-site) is designed for intermediate-level practitioners who wish 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 generate AI-driven insights.
• Optimise models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

• Lectures supported by live demonstrations and interactive discussion.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises within a guided lab environment.

Course Customisation Options

• For tailored training aligned with specific healthcare devices or regulatory workflows, please contact us to customise the programme.

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

This instructor-led, live training (delivered online or on-site) is designed for learners at beginner to intermediate levels who wish to build functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

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

• Collect and prepare data for TinyML projects.
• Train and optimise small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop end-to-end embedded AI prototypes.

Course Format

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

Course Customisation Options

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