TinyML for IoT Applications Training Course

Delve deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing in a variety of business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

TinyML involves the implementation of optimized machine learning models on edge devices with limited resources.

This instructor-led training, available both online and onsite, is designed for advanced technical professionals aiming to design, optimize, and deploy comprehensive TinyML workflows.

Upon completing this training, participants will acquire the skills to:

• Gather, prepare, and handle datasets tailored for TinyML applications.
• Train and optimize models for power-efficient microcontrollers.
• Transform models into lightweight formats appropriate for edge devices.
• Deploy, evaluate, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical laboratory sessions and iterative experimentation.
• Hands-on deployment on platforms based on microcontrollers.

Options for Course Customization

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please get in touch to make arrangements.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for 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 Greece (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 Greece (online or onsite) is designed for product managers and developers seeking to decentralize data management for improved performance, leveraging smart devices located at the source network.

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Recognize use cases and examples suitable for Edge Computing implementation.
• Design and construct Edge Computing solutions to accelerate data processing and lower operational expenses.

Establish a solid foundation in designing and managing a resilient edge computing infrastructure. Gain insights into open hybrid cloud systems, workload management across diverse clouds, and the importance of flexibility and redundancy. This course provides essential knowledge for building scalable and secure infrastructure that supports the dynamic needs of modern applications through edge computing.

This instructor-led, live training in Greece (available online or on-site) is designed for intermediate-level professionals seeking to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the principles and advantages of Federated Learning in IoT and edge computing contexts.
• Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Overcome challenges related to data privacy and network limitations in IoT systems.

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment tailored for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Utilizing TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

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

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

After completing this training, participants will be capable of:

• Utilizing quantization, pruning, and compression methods to minimize model size while maintaining accuracy.
• Benchmarking TinyML models regarding latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Assessing 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.

Course Customization Options

• For bespoke 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 looking to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be equipped 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 constrained environments.

Course Format

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

Course Customization Options

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

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

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

• Comprehend the core principles of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power consumption.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML enables the deployment of machine learning models on low-power microcontrollers and embedded platforms, particularly within the realms of robotics and autonomous systems.

This instructor-led live training, available both online and on-site, is designed for advanced professionals looking to embed TinyML-driven perception and decision-making into autonomous robots, drones, and intelligent control systems.

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

• Design optimized TinyML models tailored for robotic applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions into established robotic control frameworks.
• Deploy and validate lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures accompanied by interactive discussions.
• Hands-on labs centered around embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Course Customization Options

• Customization is available to accommodate organization-specific robotics environments, upon request.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers seeking to implement TinyML techniques for AI-driven 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 session (available online or onsite) is designed for intermediate-level practitioners who aim to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be equipped to:

• Design and deploy TinyML models for the real-time processing of health data.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for wearables with limited power and memory capacity.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures augmented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks within a guided laboratory environment.

Customization Options for the Course

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