TinyML for IoT Applications 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.

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 intermediate-level IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

• Grasp the core principles of IoT and edge computing and their significance in digital transformation.
• Recognize key use cases for IoT and edge computing within manufacturing, logistics, and energy sectors.
• Distinguish between edge and cloud computing architectures and their respective deployment scenarios.
• Deploy edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Brazil (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing 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 Brazil (online or onsite) is designed for product managers and developers who aim to use Edge Computing to decentralize data management for improved performance by leveraging smart devices located on the source network.

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

• Gaining a solid understanding of the fundamental concepts and benefits of Edge Computing.
• Identifying practical use cases and examples where Edge Computing is applicable.
• Designing and implementing Edge Computing solutions to accelerate data processing and lower operational expenses.

This instructor-led, live training in Brazil (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

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.

This instructor-led, live training in Brazil (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

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.

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.