TinyML Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers looking to implement TinyML techniques for AI-driven 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 on microcontrollers.
• Optimize AI inference processes for minimal power consumption.
• Integrate TinyML solutions into practical IoT applications.

TinyML represents a machine learning methodology tailored for compact devices with limited resources.

This guided, live training session (available online or in-person) is designed for beginner to intermediate learners interested in developing functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

After finishing this training, participants will acquire the ability to:

• Gather and prepare data for TinyML initiatives.
• Train and refine small-scale machine learning models suitable for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and compatible hardware boards.
• Create complete end-to-end embedded AI prototypes.

Course Format

• Instructor-led presentations coupled with guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab projects using actual hardware.

Customization Options

• For personalized training aligned with your specific hardware or application needs, please reach out to us to arrange.

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

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

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

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimize 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.

Course Customization Options

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

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led training session (available online or onsite) is designed for advanced professionals aiming 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.

TinyML serves as a framework for deploying machine learning models on low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) targets advanced-level professionals looking to integrate TinyML-based 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 tailored 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.

TinyML serves as a framework for implementing machine learning models on low-power, resource-constrained devices in the field.

This instructor-led live training (available online or onsite) is tailored for intermediate-level professionals seeking to apply TinyML techniques to smart agriculture solutions that improve automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Construct and deploy TinyML models for agricultural sensing applications.
• Incorporate edge AI into IoT ecosystems for automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Establish 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 involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, is designed for intermediate-level professionals 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 derive AI-driven insights.
• Optimize models to meet the low-power and memory constraints of wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

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

Customization Options

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

TinyML involves deploying machine learning models onto hardware with severe resource limitations.

This instructor-led live training, available online or onsite, targets advanced practitioners seeking to optimize TinyML models for low-latency and 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 to assess latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Assessing the balance between performance, accuracy, and hardware limitations.

Course Format

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

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training in Belgrade (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.

This instructor-led, live training in Belgrade (online or on-site) is designed for 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 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 real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This guided, live training in Belgrade (available online or in-person) is designed for beginner-level engineers and data scientists who want to understand the fundamentals of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

• Grasping the core principles of TinyML and its importance.
• Implementing lightweight AI models on microcontrollers and edge devices.
• Optimizing and fine-tuning machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios including gesture recognition, anomaly detection, and audio processing.