TinyML with Raspberry Pi and Arduino Training Course

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

This instructor-led live training, available online or onsite, targets advanced technical professionals who want to design, optimize, and deploy comprehensive TinyML pipelines.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical laboratory exercises 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 a customized session.

This instructor-led, live training in Bulgaria (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 completion of this training, participants will be able to:

• Grasp the fundamentals 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.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Enhance AI models for better power efficiency and memory utilization.

TinyML involves the deployment of machine learning models on hardware with severely limited resources.

This instructor-led live training, available online or onsite, 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 minimize model size while preserving accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware limitations.

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 customized training aligned with specific hardware platforms or internal workflows, please contact us to tailor 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 constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing 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 needs.

This instructor-led, live training in Bulgaria (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

Upon completing 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 for deploying 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 either online or onsite, targets advanced professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be equipped to:

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

Course Format

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

Customization Options

• For organizations with specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in Bulgaria (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who aim to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Grasping the core principles of TinyML and edge AI.
• Implementing lightweight AI models on microcontrollers.
• Enhancing AI inference for minimal power usage.
• Incorporating TinyML into practical IoT solutions.

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

This instructor-led training, available online or onsite, targets intermediate-level practitioners looking to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

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

This instructor-led, live training in Bulgaria (online or onsite) is aimed at 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 represents a framework for deploying machine learning models on low-power, resource-constrained devices directly in the field.

This instructor-led live training (available online or onsite) targets intermediate-level professionals aiming to apply TinyML techniques to smart agriculture solutions, thereby enhancing automation and environmental intelligence.

Upon completing this program, participants will be able to:

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

Format of the Course

• Guided presentations complemented by applied technical discussions.
• Hands-on practice utilizing real-world datasets and devices.
• Practical experimentation conducted within a supported lab environment.

Course Customization Options

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