TinyML Training in Patras

This instructor-led, live training in Patras (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 represents a machine learning methodology specifically optimized for small, resource-constrained devices.

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

Upon completion of this training, participants will acquire the ability to:

• Gather and prepare data for TinyML initiatives.
• Train and fine-tune compact machine learning models for microcontroller environments.
• Deploy TinyML models onto Raspberry Pi, Arduino, and related development 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 utilizing real hardware.

Customization Options

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

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.

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.

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.

TinyML provides a framework for deploying machine learning models on low-power, resource-constrained devices in the field.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

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

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

Format of the Course

• Guided presentations and applied technical discussion.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation in a supported lab environment.

Course Customization Options

• For tailored training aligned with specific agricultural systems, please contact us to customize the program.

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.

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.

This instructor-led, live training in Patras (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners aiming to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the core concepts of TinyML and its practical applications within IoT.
• Establish a TinyML development environment tailored for IoT projects.
• Create and deploy ML models onto low-power microcontrollers.
• Utilize TinyML to implement predictive maintenance and anomaly detection solutions.
• Optimize TinyML models to ensure efficient power consumption and memory utilization.

This instructor-led, live training in Patras (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.

This instructor-led, live training in Patras (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.