TinyML for Smart Agriculture Training Course

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 (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 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.