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

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

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

• Grasp the fundamentals of 5G technology and its influence 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 to ensure efficient performance on edge devices.

The '6G and the Intelligent Edge' course offers a forward-looking exploration into the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-driven data processing. This integration is designed to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training is available both online and onsite. It is tailored for intermediate-level IT architects seeking to comprehend and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be equipped to:

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

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 customized training for this course, please contact us to make arrangements.

This instructor-led, live training in South Africa (online or onsite) 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 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 South Africa (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who wish to acquire practical skills in deploying AI models on edge devices for a variety of applications.

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

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

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

This instructor-led, live training (available online or onsite) targets advanced technical professionals who wish to design, optimise, and deploy complete TinyML pipelines.

Upon completing this training, participants will have learned how 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 on real hardware.

Course Format

• Instructor-guided 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 South Africa (online or onsite) 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.

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities within Edge AI deployments.
• Implement encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

This instructor-led, live training in South Africa (online or onsite) is aimed at 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 benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

TinyML involves the deployment of machine learning models onto hardware with significant resource constraints.

This instructor-led, live training (available online or on-site) is designed for advanced practitioners seeking 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 compromising 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 involves the deployment of machine learning models onto low-power, resource-constrained devices operating at the edge of the network.

This instructor-led live training, available either online or onsite, is designed for advanced professionals seeking 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.

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.

This instructor-led, live training in South Africa (online or onsite) is aimed at beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

By the end 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.
• Optimise 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 commonly used in robotics and autonomous systems.

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

Upon completing this course, participants will be equipped 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 combined with interactive discussions.
• Hands-on labs focused on embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Course 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 session (available online or onsite) is designed for intermediate-level professionals looking 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.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Format of the Course

• Lectures supplemented by live demonstrations and interactive discussions.
• Hands-on practice with 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 South Africa (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 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 serves as a framework for deploying machine learning models onto low-power, resource-constrained devices situated in the field.

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

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

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

Format of the Course

• 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.