TinyML for Smart Agriculture Training Course

TinyML is the practice of deploying optimized machine learning models on resource-constrained edge devices.

This instructor-led, live training (online or onsite) is designed for advanced-level technical professionals who wish to design, optimize, and deploy complete TinyML pipelines.

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

• Collect, prepare, and manage datasets for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real hardware environments.

Format of the Course

• Instructor-guided 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 for arrangements.

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

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

TinyML involves deploying machine learning models on devices with very limited resources.

This instructor-led, live training (available online or onsite) is designed for advanced practitioners who aim to optimize TinyML models for efficient and low-latency deployment on embedded devices.

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

• Apply quantization, pruning, and compression techniques to minimize model size without compromising accuracy.
• Evaluate TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware limitations.

Format of the Course

• Instructor-led presentations enhanced by technical demonstrations.
• Practical exercises for optimizing models and comparing their performance.
• Hands-on implementation of TinyML pipelines in a controlled laboratory setting.

Course Customization Options

• For customized training that aligns with specific hardware platforms or internal processes, please contact us to tailor the program.

TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices that operate at the network edge.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who aim to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

By the end 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.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in resource-limited environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Course Customization Options

• Organizations can request a customized version of this training to align with their specific security and compliance requirements.

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

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

TinyML is a framework designed for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

This instructor-led, live training (available both online and onsite) is targeted at advanced-level professionals who want 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 for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML 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 Uzbekistan (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish 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.

TinyML involves integrating machine learning into low-power, resource-limited wearable and medical devices.

This instructor-led, live training (available online or on-site) is designed for intermediate-level practitioners who want to implement TinyML solutions for healthcare monitoring and diagnostic applications.

After completing 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 gain AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-generated outputs.

Format of the Course

• Lectures enhanced by live demonstrations and interactive discussions.
• Hands-on practice with data from wearable devices and TinyML frameworks.
• Implementation exercises in a guided laboratory environment.

Course Customization Options

• For customized training that aligns with specific healthcare devices or regulatory processes, please contact us to tailor the program.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

TinyML is a machine learning methodology designed for small devices with limited resources.

This instructor-led, live training (available both online and on-site) is targeted at learners ranging from beginners to intermediates who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

By the end of this training, participants will have acquired the skills to:

• Gather and prepare data for TinyML projects.
• Train and optimize compact machine learning models suitable for microcontroller environments.
• Deploy TinyML models on devices such as Raspberry Pi, Arduino, and related boards.
• Create end-to-end embedded AI prototypes.

Format of the Course

• Guided presentations by an instructor and interactive discussions.
• Practical exercises and hands-on experimentation.
• Project work on real hardware in a live-lab setting.

Course Customization Options

• For customized training that aligns with your specific hardware or use case, please contact us to arrange.