TinyML for Smart Agriculture Training Course

This instructor-led training in Uzbekistan empowers advanced professionals to design, optimize, and deploy full-scale TinyML pipelines. Through practical labs, participants will learn to collect data, train low-power models, and validate real-world applications.

This instructor-led, live training session in Uzbekistan (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers utilizing TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI solutions.
• Configuring a development environment suitable for TinyML initiatives.
• Training, optimizing, and deploying AI models on power-efficient microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to create practical TinyML applications.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

This instructor-led, live training in Uzbekistan prepares advanced practitioners to fine-tune TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning techniques, implement low-latency inference pipelines, and benchmark performance against memory and energy constraints.

This instructor-led course in Uzbekistan empowers advanced professionals to secure TinyML pipelines on edge devices. Participants will gain the skills to apply privacy-preserving methods, defend models against adversarial threats, and enforce secure data handling practices in constrained environments.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power consumption.
• Implement TinyML solutions for real-world use cases, including gesture recognition, anomaly detection, and audio processing.

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

This instructor-led live training in Uzbekistan (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.

This live, instructor-led training in Uzbekistan focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will master the design of models for real-time health data, learn to optimize them for low-power wearables, and ensure clinical reliability. The course includes practical, hands-on lab exercises to reinforce learning.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to apply TinyML for predictive maintenance, anomaly detection, and smart sensor solutions.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and its role in IoT ecosystems.
• Configure a TinyML development environment tailored for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML techniques for predictive maintenance and anomaly detection.
• Refine TinyML models to achieve optimal power efficiency and memory utilization.