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

This instructor-led, live training in Brazil (online or in person) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks speed up Edge AI applications.

Upon completing 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 in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize 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 Brazil (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their 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 for developing and optimizing Edge AI models.
• Implement leading-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Investigate innovative use cases and emerging trends in Edge AI.
• Navigate advanced ethical and security considerations in Edge AI deployments.

This instructor-led, live training in Brazil (online or in-person) is designed for intermediate-level developers, data scientists, and technology enthusiasts who want to acquire practical skills for deploying AI models on edge devices for various applications.

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

• Grasping the core principles of Edge AI and its advantages.
• Configuring and setting up the edge computing environment.
• Creating, training, and refining AI models for edge deployment.
• Executing practical AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed on the edge.
• Tackling ethical and security issues related to Edge AI applications.

TinyML refers to the deployment of optimized machine learning models on edge devices with limited resources.

This instructor-led live training (available online or on-site) is designed for advanced technical professionals aiming to design, optimize, and deploy complete TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets specifically for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models into lightweight formats suitable 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 arrange it.

This instructor-led, live training in Brazil (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers looking to implement strong security measures and resilience strategies for Edge AI systems.

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

This instructor-led, live training in Brazil (online or onsite) targets 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 onto hardware with severe resource limitations.

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

After completing this training, participants will be able to:

• Apply quantization, pruning, and compression techniques to minimize model size while maintaining accuracy.
• Benchmark TinyML models regarding latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware constraints.

Course Format

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

Customization Options

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

TinyML involves deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led live training (available online or onsite) is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be able to:

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

Course Format

• Interactive lectures accompanied by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML platforms.

Customization Options

• Organizations can request a customized version of this training to align with their specific security and compliance requirements.

This instructor-led, live training in Brazil (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.
• Optimize 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 enables the deployment of machine learning models on low-power microcontrollers and embedded platforms, which are essential for robotics and autonomous systems.

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

Upon completion of this course, participants will be able 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 paired with interactive discussions.
• Hands-on labs centered 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 represents the fusion of machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, targets intermediate practitioners seeking to deploy TinyML solutions for healthcare monitoring and diagnostic use cases.

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

• Design and deploy TinyML models tailored for real-time health data processing.
• Collect, preprocess, and analyze biosensor data to generate AI-driven insights.
• Optimize models to function efficiently on low-power and memory-limited wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided lab sessions for implementation exercises.

Customization Options

• For bespoke training aligned with specific healthcare devices or regulatory workflows, please contact us to tailor the program.

This instructor-led, live training in Brazil (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI practitioners looking to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the fundamentals of TinyML and its role in IoT.
• Configure a TinyML development environment for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to ensure efficient power and memory usage.

TinyML represents a machine learning approach specifically optimized for small devices with limited resources.

This instructor-led training, available online or onsite, is designed for beginners to intermediate learners who want to develop functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, participants will be able to:

• Gather and prepare data for TinyML initiatives.
• Train and optimize lightweight machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Create end-to-end embedded AI prototypes.

Course Format

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

Customization Options

• For customized training tailored to your specific hardware or use case, please contact us to make arrangements.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices in field environments.

This instructor-led training (available online or onsite) is tailored for intermediate professionals aiming to leverage TinyML techniques for smart agriculture solutions that boost automation and environmental intelligence.

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

• Create and deploy TinyML models tailored for agricultural sensing.
• Incorporate edge AI into IoT ecosystems to automate crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Establish workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

• Guided presentations paired with applied technical discussions.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation within a supported lab environment.

Course Customization Options

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