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

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to explore 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 for efficient performance on edge devices.

This forward-looking course investigates the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

Designed for intermediate-level IT architects, this live, instructor-led training (available online or onsite) helps participants understand and design next-generation distributed architectures that leverage the powerful synergy between 6G connectivity and intelligent edge systems.

By the end of this course, participants will be equipped to:

• Comprehend the transformative impact of 6G on edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G.
• Evaluate the business and operational models facilitated by the convergence of 6G and edge technologies.

Course Format

• Interactive lectures and guided discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations using optional edge or container tools.

Customization Options

• For customized training arrangements, please contact us directly.

This instructor-led, live training in Argentina (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 live, instructor-led training in Argentina (online or on-site) is designed for intermediate developers, data scientists, and technology enthusiasts seeking to acquire practical expertise in deploying AI models on edge devices across diverse applications.

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

• Grasping the foundational principles of Edge AI and its advantages.
• Establishing and configuring the edge computing infrastructure.
• Creating, training, and refining AI models for edge deployment.
• Executing practical AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed at the edge.
• Navigating ethical and security challenges associated with Edge AI applications.

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 designed for advanced-level cybersecurity experts, AI engineers, and IoT developers seeking to implement strong security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication methods to ensure data protection.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

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.

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.

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.

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want 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 IoT applications.
• Configure 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 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.