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

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to explore how 5G networks enhance Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

6G and the Intelligent Edge offers a forward-looking exploration into how 6G wireless technologies integrate with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT architects looking to understand and design next-generation distributed architectures by leveraging the synergy between 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Grasp how 6G will transform 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 enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using 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 Italy (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 Italy (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts who want to gain practical skills in deploying AI models on edge devices for diverse applications.

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

• Grasp the core principles of Edge AI and its advantages.
• Configure and set up the edge computing environment.
• Develop, train, and optimize AI models specifically for edge deployment.
• Implement functional AI solutions on edge devices.
• Assess and enhance the performance of models deployed at the edge.
• Tackle ethical and security aspects related to Edge AI applications.

TinyML involves deploying machine learning models optimized for resource-constrained edge devices.

This live, instructor-led training, available online or on-site, targets advanced technical professionals aiming to design, optimize, and implement complete TinyML pipelines.

Upon completing this course, participants will be able to:

• Gather, prepare, and manage datasets tailored for TinyML applications.
• Train and fine-tune models for low-power microcontrollers.
• Convert models into lightweight formats compatible with 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 with specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in Italy (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement robust 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.
• Apply encryption and authentication techniques to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

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

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

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

TinyML involves deploying machine learning models onto hardware with strict resource limitations.

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

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to reduce model size while maintaining accuracy.
• Benchmark TinyML models to assess 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 complemented 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 training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

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

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

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

Course Format

• Engaging lectures supported by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML frameworks.

Course Customization Options

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

This instructor-led, live training in Italy (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 represents a framework designed to deploy machine learning models on low-power microcontrollers and embedded platforms utilized within robotics and autonomous systems.

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

Upon completing this course, participants will be capable of:

• Designing optimized TinyML models specifically for robotics applications.
• Implementing on-device perception pipelines to achieve real-time autonomy.
• Integrating TinyML solutions into existing robotic control frameworks.
• Deploying and testing 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

• Customization for organization-specific robotics environments can be arranged upon request.

TinyML represents the fusion of machine learning capabilities with low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals looking to deploy TinyML solutions for healthcare monitoring and diagnostic purposes.

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

• Designing and deploying TinyML models to process health data in real time.
• Collecting, preprocessing, and interpreting biosensor data to generate AI-driven insights.
• Optimizing models to function efficiently on wearable devices with limited power and memory.
• Assessing the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

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

Customization Options

• To arrange customized training that aligns with specific healthcare devices or regulatory workflows, please contact us.

This instructor-led, live training in Italy (online or onsite) targets 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:

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

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

This instructor-led live training (available online or on-site) is designed for beginner to intermediate learners who want to build functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

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

• Gather and prepare data for TinyML initiatives.
• Train and optimize compact machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and associated 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.

Course Customization Options

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

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) is tailored for intermediate-level professionals looking to apply TinyML techniques to smart agriculture solutions that boost automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems to enable automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

• Guided presentations and 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.