TinyML for IoT Applications Training Course

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

This instructor-led training session, available either online or on-site, is designed for advanced technical professionals who aim to design, optimize, and implement comprehensive TinyML pipelines.

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

• Gather, preprocess, and manage datasets specifically for TinyML use cases.
• Train and refine models to run efficiently on low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and oversee TinyML applications in actual hardware settings.

Course Format

• Lectures guided by an instructor accompanied by technical discussions.
• Practical laboratory exercises and iterative experimentation.
• Hands-on deployment experience on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please get in touch with us to arrange.

This instructor-led, live training in Argentina (online or onsite) is tailored for intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across diverse industries.

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

• Comprehend the foundational principles of IoT and edge computing and their significance in digital transformation.
• Identify practical use cases for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures along with their deployment scenarios.
• Deploy edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level developers, system architects, and industry professionals aiming to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

By the conclusion 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 Argentina (online or onsite) targets product managers and developers who aim to utilize 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 able to:

• Comprehend the fundamental concepts and benefits of Edge Computing.
• Recognize specific use cases and practical examples suitable for Edge Computing implementation.
• Design and construct Edge Computing solutions aimed at accelerating data processing and lowering operational expenses.

Delivered by an instructor via live online or onsite sessions in Argentina, this training targets intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the fundamental principles and advantages of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices for decentralized AI processing.
• Minimize latency and enhance real-time decision-making within edge computing environments.
• Tackle challenges concerning data privacy and network limitations in IoT systems.

This instructor-led, live training Argentina (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers aiming to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be equipped to:

• Grasp the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

In this instructor-led live training in Argentina, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and understand how they integrate to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

TinyML involves the deployment of machine learning models onto hardware with severe resource limitations.

This instructor-led live training, available either online or on-site, is designed for 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:

• Utilize quantization, pruning, and compression methods to shrink model size while maintaining accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Deploy 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 in a controlled lab setting.

Customization Options

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

TinyML represents a methodology for 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 seeking to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be equipped to:

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

Course Format

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

Customization Options

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

This instructor-led, live training in Argentina (online or onsite) is designed for beginner-level engineers and data scientists who want to grasp the fundamentals of TinyML, explore its potential applications, and learn to deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and recognize its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power consumption.
• Utilize TinyML for practical applications such as gesture recognition, anomaly detection, and audio processing.

TinyML is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon finishing this course, participants will be able to:

• Design optimized 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 organization-specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in Argentina (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 refers to the integration of machine learning algorithms into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available either online or on-site, targets intermediate-level professionals seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

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

• Designing and deploying TinyML models for real-time health data processing.
• Collecting, preprocessing, and interpreting biosensor data to generate AI-driven insights.
• Optimizing models specifically for low-power and memory-limited wearable devices.
• Assessing the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

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

Customization Options

• For training tailored to specific healthcare devices or regulatory workflows, please contact us to customize the program.

TinyML represents a machine learning methodology specifically designed for small, resource-limited devices.

This instructor-led live training, available both online and onsite, targets beginner to intermediate learners eager to create functional TinyML applications utilizing Raspberry Pi, Arduino, and similar microcontrollers.

By the end of this training, participants will have acquired the ability 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 hardware boards.
• Construct end-to-end embedded AI prototypes.

Course Format

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

Customization Options

• For 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 directly in the field.

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

Upon completing this program, participants will be able to:

• Construct and deploy TinyML models tailored for agricultural sensing.
• Integrate edge AI into IoT ecosystems to enable automated 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 alongside applied technical discussions.
• Hands-on practice utilizing real-world datasets and hardware.
• Practical experimentation within a supported lab environment.

Customization Options

• For training customized to align with specific agricultural systems, please contact us to tailor the program.