TinyML for IoT Applications Training Course

Delve deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing in a variety of business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

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.

This instructor-led, live training in Serbia (online or onsite) is targeted at intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enhancing efficiency, real-time processing, and innovation across various industries.

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

• Understand the 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.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.

This instructor-led live training in Serbia (online or onsite) targets intermediate-level developers, system architects, and industry professionals eager to leverage Edge AI to boost IoT applications with intelligent data processing and analytics.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in 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 Serbia (online or onsite) is designed for product managers and developers aiming to decentralize data management via Edge Computing to achieve faster performance, utilizing smart devices located within the source network.

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

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

Establish a solid foundation in designing and managing resilient edge computing infrastructure. Gain insights into open hybrid cloud setups, workload management across various clouds, and strategies for ensuring flexibility and redundancy. This training equips learners with the essential knowledge needed to build scalable, secure infrastructure capable of supporting the dynamic demands of modern applications through edge computing.

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

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

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

This instructor-led, live training in Serbia (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.

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.

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.

This guided, live training in Serbia (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.

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.

This instructor-led, live training in Serbia (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 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.