TinyML with Raspberry Pi and Arduino Training Course

TinyML involves deploying optimized machine learning models onto edge devices with limited resources.

This guided, live training session (available online or on-site) targets advanced technical professionals looking to design, optimize, and deploy complete TinyML pipelines.

Upon completing this course, participants will be able to:

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimize models specifically for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Guided lectures and technical discussions led by an instructor.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in the Netherlands (online or onsite) targets 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 capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment suitable for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Leveraging 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 guided, live training session (available online or on-site) targets advanced professionals looking to optimize TinyML models for low-latency and memory-efficient execution on embedded devices.

After finishing this course, participants will be capable of:

• Utilizing quantization, pruning, and compression methods to decrease model size while maintaining accuracy.
• Assessing TinyML models for latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Analyzing the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led lectures complemented by technical demonstrations.
• Practical exercises focused on optimization and comparative performance testing.
• Hands-on creation of TinyML pipelines within a controlled lab setting.

Customization Options for the Course

• To receive training tailored to specific hardware platforms or internal processes, please reach out to us for program customization.

TinyML involves 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 implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be capable of:

• Recognizing security risks specific to on-device TinyML inference.
• Deploying privacy-preserving mechanisms for edge AI solutions.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying 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 guided, live training in the Netherlands (online or in person) is designed for engineers and data scientists at the beginner level who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models on microcontrollers.

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

• Grasping the core concepts of TinyML and its importance.
• Deploying compact AI models on microcontrollers and edge devices.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML represents a framework designed to deploy machine learning models onto low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

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

Upon completing this course, participants will be able to:

• Design optimized TinyML models specifically for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions 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.

This instructor-led, live training in the Netherlands (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to apply TinyML techniques for AI-enabled applications on energy-efficient hardware.

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

• Grasp the core concepts of TinyML and edge AI.
• Deploy lightweight AI models onto microcontrollers.
• Optimize AI inference to minimize power consumption.
• Integrate TinyML solutions into practical IoT applications.

TinyML involves embedding machine learning capabilities into power-efficient, resource-constrained wearable and medical devices.

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners looking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

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

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML outputs.

Course Format

• Lectures accompanied by live demonstrations and interactive discussions.
• Practical exercises using wearable device data and TinyML frameworks.
• Implementation tasks within a guided lab environment.

Customization Options

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

This instructor-led, live training in the Netherlands (online or onsite) is designed for 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:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up 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 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 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 for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems to enable automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Design workflows for precision irrigation, pest detection, and environmental analytics.

Format of the Course

• Guided presentations coupled with 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.