TinyML for IoT Applications Training Course

This instructor-led, live training in Romania (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks accelerate Edge AI applications.

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

TinyML refers to the deployment of optimized machine learning models on edge devices with limited resources.

This instructor-led, live training session (available online or onsite) is designed for advanced technical professionals seeking 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 for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures coupled with technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Course Customization Options

• For training tailored to specific toolchains, hardware boards, or internal workflows, please reach out to us to make arrangements.

This instructor-led live training in Romania (online or onsite) targets intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across different industries.

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

• Understand the foundational concepts of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Romania (online or onsite) is designed for 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.

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

• Grasp the fundamentals of Edge AI and its role in IoT.
• Set up and configure Edge AI environments tailored for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making within 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 Romania (online or onsite) targets product managers and developers aiming to utilize Edge Computing to decentralize data management for improved performance, capitalizing on smart devices at the network source.

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

• Comprehending the fundamental principles and benefits of Edge Computing.
• Recognizing specific use cases and examples suitable for Edge Computing implementation.
• Designing and constructing Edge Computing solutions to accelerate data processing and lower operational expenses.

This instructor-led, live training in Romania (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in Romania (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse. <\/p>

Upon completing this training, participants will be able to: <\/p>

• Comprehend the core principles of TinyML and its advantages for edge AI applications. <\/li>
• Configure a development environment tailored for TinyML projects. <\/li>
• Train, optimize, and deploy AI models on low-power microcontrollers. <\/li>
• Leverage TensorFlow Lite and Edge Impulse to create real-world TinyML solutions. <\/li>
• Optimize AI models to meet power efficiency requirements and memory limitations. <\/li> <\/ul>

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

This instructor-led live training, available both online and 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:

• Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy.
• Benchmark TinyML models for latency, memory consumption, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

• Instructor-led presentations supported by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customization Options

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

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 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.

Course Format

• Engaging lectures supported 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 meet their specific security and compliance needs.

This guided, live training in Romania (online or onsite) 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 implement AI models on microcontrollers.

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

• Grasping the core concepts of TinyML and why it matters.
• Implementing compact AI models on microcontrollers and edge devices.
• Tuning and refining machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios like gesture recognition, anomaly detection, and audio processing.

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

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon finishing this course, participants will be able to:

• Design optimized TinyML models for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML 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 Romania (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 able to:

• Grasp the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for minimal power consumption.
• Integrate TinyML into real-world IoT applications.

TinyML represents the convergence of machine learning with low-power, resource-constrained wearable and medical devices.

This guided, live training program—available either online or on-site—is designed for practitioners at an intermediate level who aim to implement TinyML solutions specifically for healthcare monitoring and diagnostic purposes.

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

• Create and deploy TinyML models for processing health data in real-time.
• Gather, preprocess, and analyze biosensor data to derive AI-powered insights.
• Optimize models to function efficiently on wearable devices with limited power and memory.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

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

Course Customization Options

• For customized training that aligns with specific healthcare devices or regulatory workflows, please reach out to us to tailor the program.

TinyML is a machine learning approach optimized for small, resource-constrained devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, attendees will gain the skills to:

• Collect and prepare data for TinyML projects.
• Train and optimize small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop end-to-end embedded AI prototypes.

Format of the Course

• Instructor-led presentations and guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab project work on real hardware.

Course Customization Options

• For tailored training aligned with your specific hardware or use case, please contact us to arrange.

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.