Deploying AI on Microcontrollers with TinyML 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, live training session (conducted online or on-site) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest innovations in Edge AI, optimize their models for edge deployment, and explore specialized applications across various industries.

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

• Explore sophisticated techniques for Edge AI model development and optimization.
• Apply cutting-edge strategies for deploying AI models onto edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI scenarios.
• Improve the performance and efficiency of Edge AI solutions.
• Identify innovative use cases and emerging trends within Edge AI.
• Navigate advanced ethical and security implications in Edge AI deployments.

Delivered as an instructor-led live training in Latvia (either online or onsite), this program targets intermediate developers, data scientists, and tech enthusiasts looking to build practical proficiency in deploying AI models on edge devices for diverse applications.

By the conclusion of the training, participants will be capable of:

• Comprehending the core tenets of Edge AI and its associated benefits.
• Setting up and adjusting edge computing infrastructures.
• Creating, training, and optimizing AI models tailored for edge deployment.
• Implementing real-world AI solutions on edge hardware.
• Evaluating and refining the performance of models deployed on the edge.
• Managing ethical and security aspects within Edge AI frameworks.

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 on-site) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement comprehensive security and resilience measures for Edge AI systems.

By the end of the program, participants will be equipped to:

• Analyze security risks and vulnerabilities in Edge AI deployments.
• Utilize encryption and authentication techniques for robust data protection.
• Develop resilient Edge AI architectures that can endure cyber threats.
• Apply secure AI model deployment strategies within edge environments.

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 instructor-led, live training offered in Latvia (either online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners looking to apply TinyML in predictive maintenance, anomaly detection, and smart sensor contexts.

By the end of the program, learners will have the ability to:

• Understand the core concepts of TinyML and their specific applications in IoT.
• Establish a development workspace for TinyML-based IoT projects.
• Build and run machine learning models on energy-efficient microcontrollers.
• Integrate predictive maintenance and anomaly detection workflows using TinyML.
• Optimize model performance to handle strict power and memory limitations.

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.