TinyML Training in Richmond

This instructor-led, live training in Richmond (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 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.

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.

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.

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.

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.

This instructor-led, live training in Richmond (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.

This instructor-led, live training in Richmond (online or onsite) is designed for intermediate-level embedded systems engineers and artificial intelligence 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 foundational principles of TinyML and its advantages for edge AI applications in various sectors, including those for government.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy artificial intelligence models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to develop practical TinyML applications.
• Enhance AI models for improved power efficiency and memory management.

This instructor-led, live training in Richmond (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.