TinyML for IoT Applications Training Course

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

Following this training, participants will be capable of:

• Understanding the basics of 5G technology and its effect on Edge AI.
• Implementing AI models optimized for low-latency tasks in 5G settings.
• Establishing real-time decision systems utilizing Edge AI and 5G connectivity.
• Improving AI workload efficiency on edge devices.

This 21-hour training in Latvia assists IT architects in designing next-generation distributed systems. It covers 6G, edge computing, and AI integration to achieve low-latency, scalable infrastructures. Participants will acquire practical skills for building secure, resilient, and intelligent edge architectures to satisfy future business requirements.

This instructor-led training in Latvia empowers advanced professionals to design, optimize, and implement comprehensive TinyML pipelines. Through practical laboratories, attendees will master data collection, training of low-power models, and testing of real-world applications.

This instructor-led live training in Latvia (online or onsite) is designed for intermediate-level IT professionals and business managers who aim to comprehend the potential of IoT and edge computing to enhance efficiency, enable real-time processing, and foster innovation across diverse industries.

Upon completing this training, participants will be able to:

• Understand the core principles of IoT and edge computing and their impact on digital transformation.
• Identify relevant 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 to support predictive maintenance and real-time decision-making.

This live, instructor-led training delivered Latvia (either online or onsite) is specifically designed for intermediate-level developers, system architects, and industry experts aiming to enhance IoT applications through intelligent data processing and analytics powered by Edge AI.

Following this training, participants will be proficient in:

• Comprehending the basics of Edge AI and its relevance to IoT.
• Setting up and tuning Edge AI configurations for IoT devices.
• Building and rolling out AI models on edge hardware for IoT use.
• Enabling instant data processing and decision-making within IoT architectures.
• Connecting Edge AI with diverse IoT standards and platforms.
• Managing ethical frameworks and standard practices for Edge AI in IoT.

This live, instructor-led training in Latvia (delivered online or on-site) is tailored for intermediate-level professionals seeking to apply Federated Learning to refine their IoT and edge computing solutions.

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

• Comprehend the foundational principles and strategic benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Lower latency and strengthen real-time decision-making within edge computing environments.
• Address data privacy and network constraint challenges specific to IoT systems.

This instructor-led live training in Latvia (available online or onsite) is tailored for intermediate-level embedded systems engineers and AI developers seeking to deploy machine learning models on microcontrollers via TensorFlow Lite and Edge Impulse.

By the end of the session, participants will be capable of:

• Understanding the fundamentals of TinyML and its advantages in edge AI contexts.
• Establishing a development environment for TinyML initiatives.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Implementing real-world TinyML applications using TensorFlow Lite and Edge Impulse.
• Fine-tuning AI models for optimal power efficiency and memory usage.

This live, instructor-led training in Latvia empowers advanced practitioners to optimize TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning, build low-latency inference pipelines, and assess performance against memory and energy constraints.

This instructor-led training in Latvia empowers advanced professionals to secure TinyML pipelines on edge devices. Participants will master the implementation of privacy-preserving techniques, learn to harden models against adversarial threats, and apply best practices for secure data handling in resource-constrained environments.

This live, instructor-led training session in Latvia, offered online or onsite, targets entry-level engineers and data scientists seeking to understand the fundamentals of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the core principles of TinyML and its importance.
• Deploy compact AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for reduced power usage.
• Apply TinyML to practical applications like gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Latvia guides advanced professionals in integrating TinyML into autonomous robotics. Participants will learn to design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

Targeted at intermediate-level embedded engineers, IoT developers, and AI researchers, this instructor-led live training in Latvia (online or onsite) explores the implementation of TinyML techniques for AI-driven applications on energy-efficient hardware.

By the end of the session, participants will possess the ability to:

• Comprehend the essential concepts of TinyML and edge AI.
• Implement compact AI models on microcontrollers.
• Optimize inference operations to minimize power draw.
• Integrate TinyML frameworks into real-world IoT environments.

This live, instructor-led training on Latvia focuses on deploying TinyML solutions for healthcare monitoring and diagnosis. Learners will master the design of models for real-time health data, optimization for low-power wearables, and the assurance of clinical reliability. The course includes hands-on laboratory exercises.

This 21-hour instructor-led course in Latvia enables intermediate professionals to implement TinyML for smart agriculture. It covers building lightweight models, integrating edge AI with IoT, and optimizing systems for precision irrigation and pest detection within a practical, hands-on lab environment.