Deploying AI on Microcontrollers with TinyML Training Course

This instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level developers, data scientists, and technology professionals who wish to gain practical skills in deploying artificial intelligence (AI) models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits for government operations.
• Set up and configure the edge computing environment for government use.
• Develop, train, and optimize AI models for deployment on edge devices in public sector settings.
• Implement practical AI solutions on edge devices to enhance governmental workflows.
• Evaluate and improve the performance of edge-deployed models to meet government standards.
• Address ethical and security considerations specific to Edge AI applications in a government context.

TinyML involves 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 aim to design, optimize, and deploy complete TinyML pipelines for government applications.

By the conclusion of this training, participants will learn how to:

• Collect, prepare, and manage datasets for TinyML applications in public sector environments.
• Train and optimize models for low-power microcontrollers used in government projects.
• Convert models to lightweight formats suitable for edge devices deployed by government agencies.
• Deploy, test, and monitor TinyML applications in real hardware environments within the public sector.

Format of the Course

• Instructor-guided lectures and technical discussions focused on government use cases.
• Practical labs and iterative experimentation aligned with public sector workflows.
• Hands-on deployment on microcontroller-based platforms relevant to government operations.

Course Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows for government use, please contact us to arrange.

Integrate the transformative capabilities of artificial intelligence (AI) with the agility of edge computing in this comprehensive course designed for government professionals. Participants will learn to deploy AI models directly on edge devices, covering topics from understanding convolutional neural network (CNN) architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows, governance, and accountability.

This instructor-led, live training in US Empire (online or onsite) is designed for intermediate-level developers and IT professionals who wish to gain a thorough understanding of Edge AI, from foundational concepts to practical implementation, including setup and deployment.

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

• Grasp the fundamental principles of Edge AI.
• Set up and configure Edge AI environments for government use.
• Develop, train, and optimize Edge AI models.
• Deploy and manage Edge AI applications in a secure and efficient manner.
• Integrate Edge AI with existing systems and workflows to enhance operational effectiveness.
• Address ethical considerations and best practices in the implementation of Edge AI for government operations.

This instructor-led, live training in US Empire (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with advanced data processing and analytics capabilities.

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

• Understand the foundational principles of Edge AI and its application in IoT systems for government.
• Set up and configure Edge AI environments suitable for IoT devices used in public sector workflows.
• Develop and deploy AI models on edge devices to support IoT applications aligned with government objectives.
• Implement real-time data processing and decision-making capabilities in IoT systems for enhanced governance and accountability.
• Integrate Edge AI with various IoT protocols and platforms commonly used in the public sector.
• Address ethical considerations and best practices in Edge AI deployment for government applications.

This instructor-led, live training in US Empire (online or onsite) is aimed at beginner-level developers and IT professionals who wish to gain a foundational understanding of Edge AI and its introductory applications for government.

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

• Understand the basic concepts and architecture of Edge AI as they apply to public sector workflows.
• Set up and configure Edge AI environments in alignment with government standards.
• Develop and deploy simple Edge AI applications that enhance governance and accountability.
• Identify and understand the use cases and benefits of Edge AI for government operations.

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

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

Upon completing this training, participants will be able to:

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

Format of the Course

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customization Options

• For tailored training aligned with specific hardware platforms or internal workflows for government, please contact us to customize the program.

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

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

At the conclusion of this course, participants will be able to:

• Identify security risks unique to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

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

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs for government.

This instructor-led, live training in US Empire (online or onsite) is designed for government engineers and data scientists at the beginner level who wish to gain a foundational understanding of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the core principles of TinyML and their importance for government use cases.
• Deploy lightweight AI models on microcontrollers and edge devices for government applications.
• Optimize and fine-tune machine learning models to ensure low-power consumption in public sector environments.
• Apply TinyML for real-world government 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 utilized in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) is targeted at advanced-level professionals who aim to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems for government applications.

Upon completing this course, participants will be able to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to support 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 to enhance understanding.
• Hands-on labs focusing on embedded robotics tasks to provide practical experience.
• Practical exercises simulating real-world autonomous workflows to ensure readiness for deployment.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request to align with specific needs and objectives.

This instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level embedded engineers, Internet of Things (IoT) developers, and artificial intelligence (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 foundational principles of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers for government use.
• Optimize AI inference to ensure low-power consumption.
• Integrate TinyML with real-world IoT applications in public sector environments.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications for government.

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

Format of the Course

• Lectures supported by live demonstrations and interactive discussions.
• Hands-on practice with wearable device data and TinyML frameworks.
• Implementation exercises in a guided lab environment.

Course Customization Options

• For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program for government.

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

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

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