TinyML for IoT Applications Training Course

This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

By the end 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 involves the deployment of optimized machine learning models on edge devices with limited resources.

This live training, led by an instructor and available online or in-person, is designed for advanced technical professionals aiming to design, optimize, and deploy comprehensive TinyML pipelines.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-led lectures accompanied by technical discussions.
• 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 make arrangements.

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level IT professionals and business managers seeking to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

• Understand the core principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• 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 Turkey (online or onsite) is tailored for intermediate-level developers, system architects, and industry professionals seeking to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Grasping the fundamentals of Edge AI and its role in IoT.
• Setting up and configuring Edge AI environments for IoT devices.
• Developing and deploying AI models on edge devices for IoT solutions.
• Implementing real-time data processing and decision-making capabilities in IoT systems.
• Integrating Edge AI with various IoT protocols and platforms.
• Addressing ethical considerations and adhering to best practices for Edge AI in IoT.

This instructor-led, live training in Turkey (online or onsite) is designed for product managers and developers who aim to decentralize data management for improved performance by leveraging smart devices on the source network.

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

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level professionals aiming to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the principles and advantages of applying Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities within edge computing environments.
• Overcome challenges associated with data privacy and network limitations in IoT systems.

This instructor-led, live training in Turkey (online or onsite) targets intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Grasp the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhance AI models for power efficiency and address memory limitations.

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

This instructor-led training session, available both online and onsite, targets advanced practitioners seeking to optimize TinyML models for deployment on embedded devices, focusing on low latency and memory efficiency.

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

• Utilize quantization, pruning, and compression strategies to minimize model size while maintaining accuracy.
• Benchmark TinyML models to assess 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

• Instructional presentations complemented by technical demonstrations.
• Practical exercises in optimization and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab setting.

Customization Options

• To tailor the training to specific hardware platforms or internal workflows, please reach out to us for program customization.

TinyML involves deploying machine learning models onto low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training, available online or onsite, is designed for advanced professionals aiming to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy 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 Turkey (available online or onsite) is designed for beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

By the end of 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 serves as a framework for deploying 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) targets advanced-level professionals seeking 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 achieve real-time autonomy.
• Integrate TinyML solutions into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Course Format

• 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 Turkey (available online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers seeking to implement TinyML techniques for AI-powered 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 involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

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

Upon completion of this course, participants will be equipped 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 for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises conducted 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.

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 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 professionals seeking to apply TinyML techniques to smart agriculture solutions that boost automation and environmental intelligence.

Upon completing this program, participants will be able to:

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

Course Format

• 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 customized training aligned with specific agricultural systems, please contact us to tailor the program.