TinyML Training in Christchurch

This instructor-led, live training in Christchurch (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 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.

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.

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.

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.

TinyML is a framework for deploying machine learning models on low-power, resource-constrained devices in the field.

This instructor-led, live training (online or on-site) is designed for intermediate-level professionals who wish to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

Upon completing this programme, participants will gain the ability to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Use specialised tools to train and optimise lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

• Guided presentations and applied technical discussion.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation in a supported lab environment.

Course Customisation Options

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

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 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.

This instructor-led, live training in Christchurch (online or on-site) is designed for 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.
• Optimise TinyML models for efficient power and memory usage.

This instructor-led, live training in Christchurch (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.

This instructor-led, live training in Christchurch (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.