Online or onsite, instructor-led live TinyML training courses demonstrate through interactive hands-on practice how to use machine learning on ultra-low-power devices to enable AI-driven applications in resource-constrained environments.
TinyML training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Richmond Hill onsite live TinyML trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Richmond Hill - The Business Exchange
9225 Leslie Street, Richmond Hill, Canada, L4B 3H6
Place your business in the peaceful surroundings of Richmond Hill, home to leading global brands. Located in the northern suburbs, our The Business Exchange workspace is just a 30-minute drive from central Toronto and the International Airport.
This instructor-led, live training in Richmond Hill (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 is a machine learning approach optimized for small, resource-constrained devices.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.
Upon completing this training, attendees will gain the skills to:
Collect and prepare data for TinyML projects.
Train and optimize small machine learning models for microcontroller environments.
Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
Develop end-to-end embedded AI prototypes.
Format of the Course
Instructor-led presentations and guided discussions.
Practical exercises and hands-on experimentation.
Live-lab project work on real hardware.
Course Customization Options
For tailored training aligned with your specific hardware or use case, please contact us to arrange.
TinyML is the practice of deploying optimized machine learning models on resource-constrained edge devices.
This instructor-led, live training (online or onsite) is aimed at advanced-level technical professionals who wish to design, optimize, and deploy complete TinyML pipelines.
By the conclusion of this training, participants will learn how to:
Collect, prepare, and manage datasets for TinyML applications.
Train and optimize models for low-power microcontrollers.
Convert models to 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 discussion.
Practical labs and iterative experimentation.
Hands-on deployment on microcontroller-based platforms.
Course Customization Options
To customize the training with specific toolchains, hardware boards, or internal workflows, 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.
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.
TinyML is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.
Upon finishing 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.
TinyML is a framework for deploying machine learning models on low-power, resource-constrained devices in the field.
This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.
Upon completing this program, participants will gain the ability to:
Build and deploy TinyML models for agricultural sensing applications.
Integrate edge AI into IoT ecosystems for automated crop monitoring.
Use specialized tools to train and optimize lightweight models.
Develop workflows for precision irrigation, pest detection, and environmental analytics.
Format of the Course
Guided presentations and applied technical discussion.
Hands-on practice using real-world datasets and devices.
Practical experimentation in a supported lab environment.
Course Customization Options
For tailored training aligned with specific agricultural systems, please contact us to customize the program.
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.
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 discussion.
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.
TinyML is the practice of deploying machine learning models on highly resource-constrained hardware.
This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.
Upon completing this training, participants will be able to:
Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy.
Benchmark TinyML models for latency, memory consumption, and energy efficiency.
Implement optimized inference pipelines on microcontrollers and edge devices.
Evaluate trade-offs between performance, accuracy, and hardware constraints.
Format of the Course
Instructor-led presentations supported 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, please contact us to customize the program.
This instructor-led, live training in Richmond Hill (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.
This instructor-led, live training in Richmond Hill (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.
This instructor-led, live training in Richmond Hill (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.
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