TinyML for IoT Applications Training Course

Dive deeper into the innovative domain of edge computing with this advanced course. Explore complex architectures and tackle integration challenges, preparing to fully leverage the potential of edge computing across diverse business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that address specific industry requirements.

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

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

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

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Grasp the fundamentals of Edge AI and its specific applications in IoT contexts.
• Set up and configure Edge AI environments tailored for IoT devices.
• Create and deploy AI models on edge devices for IoT solutions.
• Implement real-time data processing and decision-making mechanisms within IoT systems.
• Integrate Edge AI capabilities with diverse IoT protocols and platforms.
• Address ethical considerations and adhere to best practices for Edge AI in IoT.

This instructor-led, live training in Italy (online or onsite) is designed for product managers and developers aiming to decentralize data management for enhanced performance, utilizing smart devices located on the source network.

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Recognize specific use cases and scenarios where Edge Computing is applicable.
• Design and implement Edge Computing solutions to accelerate data processing and lower operational costs.

Establish a solid foundation in designing and managing resilient edge computing infrastructure. Gain insights into open hybrid cloud infrastructures, managing workloads across diverse cloud platforms, and ensuring flexibility and redundancy. This training provides essential knowledge for creating a scalable and secure infrastructure that supports the dynamic requirements of modern applications through edge computing.

This instructor-led, live training in Italy (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 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.

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

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

• Comprehend the core principles of TinyML and edge AI.
• Deploy lightweight AI models onto microcontrollers.
• Optimize AI inference for minimal power consumption.
• Integrate TinyML with practical IoT applications.

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.