Edge AI Training in Christchurch

This instructor-led, live training in Christchurch (online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for training AI models across multiple edge devices while preserving data privacy.

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

• Understand the principles and benefits of federated learning in Edge AI.
• Implement federated learning models using TensorFlow Federated and PyTorch.
• Optimise AI training across distributed edge devices.
• Address data privacy and security challenges in federated learning.
• Deploy and monitor federated learning systems in real-world applications.

This instructor-led, live training in Christchurch (online or onsite) is aimed at beginner-level to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.

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

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimise agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in Christchurch (available online or on-site) is designed for 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 capable of withstanding cyber threats.
• Apply secure AI model deployment strategies in edge environments.

This instructor-led, live training in Christchurch (online or on-site) is designed for beginner to intermediate-level retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions to smart checkout systems, inventory management, and personalised customer engagement.

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

• Understand how Edge AI enhances retail operations and customer experience.
• Implement AI-powered smart checkout and cashier-less payment systems.
• Optimise inventory management with real-time tracking and analytics.
• Leverage computer vision and AI to deliver personalised in-store experiences.

This instructor-led, live training in Christchurch (online or on-site) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimised for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimise AI workloads for efficient performance on edge devices.

This instructor-led, live training in Christchurch (online or on-site) is designed for intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimise lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantisation, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimised models on real edge hardware platforms.

This instructor-led, live training in Christchurch (available online or on-site) is designed for intermediate to advanced-level computer vision engineers, AI developers, and IoT professionals seeking to implement and optimise computer vision models for real-time processing on edge devices.

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

• Grasp the fundamentals of Edge AI and its applications within computer vision.
• Deploy optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and the NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

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.

This instructor-led, live training in Christchurch (delivered online or on-site) is designed for intermediate to advanced-level robotics engineers, AI developers, and automation specialists seeking to implement Edge AI in robotics applications.

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

• Comprehend the role of Edge AI in autonomous systems.
• Deploy AI models on edge devices to support real-time robotics operations.
• Optimise AI performance to achieve low-latency decision-making.
• Integrate computer vision and sensor fusion technologies to enhance robotic autonomy.

Edge & Lightweight Agents is a hands-on course dedicated to deploying agentic AI workloads on resource-constrained devices. Participants will learn to build, optimise, and manage lightweight agents capable of local reasoning and inference, thereby enhancing speed, privacy, and reliability within distributed environments. The course places a strong emphasis on performance tuning, low-latency design, and the integration of hardware and software.

This instructor-led, live training (available online or on-site) is tailored for intermediate-level professionals who wish to implement and optimise on-device agentic systems using Python and edge AI frameworks.

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

• Understand the architecture and challenges associated with running agentic AI on edge devices.
• Design lightweight agent loops suitable for constrained environments.
• Implement local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
• Integrate agents with sensors, actuators, and IoT platforms.
• Optimise performance, energy consumption, and latency for real-time operation.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on development in local or emulated environments.
• Project-based learning supported by guided implementation exercises.

Course Customisation Options

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

This instructor-led, live training in Christchurch (online or on-site) is aimed at advanced-level AI engineers, embedded developers, and hardware engineers who wish to implement AI models on low-power devices while minimising energy consumption.

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

• Understand the challenges of running AI on energy-efficient devices.
• Optimise neural networks for low-power inference.
• Utilise quantisation, pruning, and model compression techniques.
• Deploy AI models on edge hardware with minimal power usage.

This instructor-led, live training in Christchurch (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimise and deploy AI models on NVIDIA Jetson platforms for edge applications.

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

• Understand the fundamentals of edge AI and NVIDIA Jetson hardware.
• Optimise AI models for deployment on edge devices.
• Use TensorRT for accelerating deep learning inference.
• Deploy AI models using JetPack SDK and ONNX Runtime.

This instructor-led, live training in Christchurch (online or on-site) is designed for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimise AI models for edge deployment.

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

• Understand the challenges and requirements involved in deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Use quantisation methods to improve model efficiency on edge hardware.
• Implement pruning and other optimisation techniques to enhance model performance.
• Deploy optimised AI models across a range of edge devices.

This instructor-led, live training in Christchurch (online or on-site) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish 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 optimise 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.

This instructor-led, live training in Christchurch (available online or on-site) is designed for intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.

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

• Understand the role of Edge AI in financial services.
• Implement fraud detection systems using Edge AI.
• Enhance customer service through AI-driven solutions.
• Apply Edge AI for risk management and decision-making.
• Deploy and manage Edge AI solutions in financial environments.

This instructor-led, live training in Christchurch (online or on-site) is designed for intermediate-level industrial engineers, manufacturing professionals, and AI developers who aim to implement Edge AI solutions in industrial automation.

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

• Understand the role of Edge AI in industrial automation.
• Implement predictive maintenance solutions using Edge AI.
• Apply AI techniques for quality control in manufacturing processes.
• Optimise industrial processes using Edge AI.
• Deploy and manage Edge AI solutions in industrial environments.

Edge AI involves deploying artificial intelligence models directly on devices and machines at the edge of the network, enabling real-time decision-making with minimal latency.

This instructor-led, live training (available online or on-site) is designed for advanced-level embedded and IoT professionals seeking to deploy AI-powered logic and control systems within manufacturing environments where speed, reliability, and offline operation are critical.

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

• Understand the architecture and benefits of edge AI systems.
• Build and optimise AI models for deployment on embedded devices.
• Use tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customisation Options

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

This instructor-led, live training in Christchurch (online or on-site) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimise AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Use tools and techniques for model conversion and optimisation.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Christchurch (available online or on-site) is designed for intermediate-level urban planners, civil engineers, and smart city project managers who wish to harness Edge AI for smart city initiatives.

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

• Understand the role of Edge AI in smart city infrastructure.
• Implement Edge AI solutions for traffic management and surveillance.
• Optimise urban resources using Edge AI technologies.
• Integrate Edge AI with existing smart city systems.
• Address ethical and regulatory considerations in smart city deployments.

This instructor-led, live training in Christchurch (available online or on-site) is designed for intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who aim to securely and ethically deploy Edge AI solutions.

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

• Understand the security and privacy challenges inherent in Edge AI.
• Implement best practices for securing edge devices and data.
• Develop strategies to mitigate security risks in Edge AI deployments.
• Address ethical considerations and ensure compliance with relevant regulations.
• Conduct security assessments and audits for Edge AI applications.

This instructor-led, live training in Christchurch (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who aim to leverage Edge AI to create innovative autonomous system solutions.

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

• Understand the role and benefits of Edge AI in autonomous systems.
• Develop and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
• Design and optimise control systems using Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.

This instructor-led, live training in Christchurch (delivered online or on-site) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to harness Edge AI to create innovative healthcare solutions.

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

• Understand the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

Edge AI empowers artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while enhancing autonomy and privacy within robotic systems.

This instructor-led, live training (available online or on-site) is designed for intermediate-level embedded developers and robotics engineers seeking to implement machine learning inference and optimisation techniques directly on robotic hardware using TinyML and edge AI frameworks.

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

• Grasp the fundamentals of TinyML and edge AI in the context of robotics.
• Convert and deploy AI models for on-device inference.
• Optimise models for speed, size, and energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Course Format

• Interactive lectures and discussions.
• Hands-on practice using TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Course Customisation Options

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

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

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

Format of the Course

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

Course Customization Options

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

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

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

• Explore advanced techniques in Edge AI model development and optimisation.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilise specialised tools and frameworks for advanced Edge AI applications.
• Optimise the 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.

Huawei's Ascend CANN toolkit delivers powerful AI inference capabilities on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimising, and deploying models in environments where compute and memory resources are limited.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI developers and integrators who wish to deploy and optimise models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Build lightweight inference pipelines using MindSpore Lite and AscendCL.
• Optimise model performance for constrained compute and memory environments.
• Deploy and monitor AI applications in real-world edge use cases.

Format of the Course

• Interactive lectures and live demonstrations.
• Hands-on laboratory work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

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

This instructor-led, live training in Christchurch (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 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 (available online or on-site) is designed for intermediate-level developers and IT professionals seeking a comprehensive understanding of Edge AI, from foundational concepts to practical implementation, including setup and deployment.

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

• Grasp the fundamental concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimise Edge AI models.
• Deploy and manage Edge AI applications.
• Integrate Edge AI with existing systems and workflows.
• Address ethical considerations and adopt best practices in Edge AI implementation.

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.

Cambricon MLUs (Machine Learning Units) are specialised AI chips optimised for inference and training in edge and datacentre scenarios.

This instructor-led, live training (online or on-site) is designed for intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and datacentre devices running the Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Hands-on application of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimisation, integration, and testing.

Course Customisation Options

• To request a customised training session for this course based on your Cambricon device model or specific use case, please contact us to make arrangements.

This instructor-led, live training in Christchurch (available online or on-site) is designed for beginner-level developers and IT professionals who want to grasp the fundamentals of Edge AI and its introductory applications.

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

• Understand the core concepts and architecture of Edge AI.
• Set up and configure Edge AI environments.
• Develop and deploy simple Edge AI applications.
• Identify and appreciate the use cases and benefits of Edge AI.