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

This instructor-led, live training in Botswana (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to investigate 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 optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimise AI workloads for efficient performance on edge devices.

"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 customized training for this course, please contact us to arrange.

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

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

• Explore advanced techniques in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize 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 Botswana (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.

TinyML refers to the deployment of highly optimised machine learning models onto edge devices with limited resources.

This guided, live training session (available online or in-person) is designed for advanced technical professionals who wish to create, optimise, and deploy full-scale TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets suitable for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Convert models into lightweight formats ideal for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

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

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements.

This instructor-led, live training in Botswana (online or onsite) targets advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.

Upon completion of this training, participants will be capable of:

• Identifying security risks and vulnerabilities inherent in Edge AI deployments.
• Implementing encryption and authentication techniques to protect data.
• Designing resilient Edge AI architectures capable of withstanding cyber threats.
• Applying secure AI model deployment strategies within edge environments.

This instructor-led, live training in Botswana (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 deploying machine learning models on hardware with strict resource limitations.

This instructor-led live training, available online or onsite, is designed for advanced practitioners looking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

After completing this training, participants will be able to:

• Utilise quantization, pruning, and compression techniques to reduce model size without compromising accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware constraints.

Course Format

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

Course Customization Options

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

TinyML refers to the practice of deploying machine learning models on low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training, available online or onsite, targets advanced-level professionals keen on securing TinyML pipelines and implementing privacy-preserving techniques within edge AI applications.

Upon completing this course, participants will be equipped to:

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

Course Format

• Engaging lectures complemented by expert-led discussions.
• Practical exercises focused on 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 Botswana (online or on-site) is designed for beginner-level engineers and data scientists who want to understand the fundamentals of TinyML, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its importance.
• Deploying streamlined AI models on microcontrollers and edge devices.
• Optimising and fine-tuning machine learning models for low-power usage.
• Applying TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML provides a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms utilised in robotics and autonomous systems.

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

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 organisation-specific robotics environments, customization 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, available online or onsite, is designed for intermediate-level practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

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 to suit low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-generated outputs.

Format of the Course

• Lectures accompanied by live demonstrations and interactive discussions.
• Hands-on practice using 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 Botswana (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 refers to a machine learning methodology designed for devices with limited resources and small form factors.

This instructor-led live training, available online or on-site, is tailored for learners at beginner to intermediate levels who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

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

• Gather and prepare data specifically for TinyML initiatives.
• Train and fine-tune compact machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and other similar boards.
• Create complete embedded AI prototypes from start to finish.

Course Format

• Instructor-led presentations combined with guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab projects utilising real hardware.

Customisation Options

• For bespoke training aligned with your specific hardware requirements or use cases, please contact us to make arrangements.

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

This instructor-led, live training (available online or onsite) 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 program, 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 specialized tools to train and optimize 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 Customization Options

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