Richmond

5811 Cooney Road, Richmond, Canada, V6X 3M1

Downtown core of Richmond, close to Vancouver International Airport, major Skytrain routes, shopping centers, banks and restaurants.

Explore Our Courses

TinyML: Running AI on Ultra-Low-Power Edge Devices

 21 Hours
This instructor-led, live training in Richmond (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.
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TinyML for IoT Applications

 21 Hours
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 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.
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Deploying AI on Microcontrollers with TinyML

 21 Hours
This instructor-led, live training in Richmond (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.
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Introduction to TinyML

 14 Hours
This instructor-led, live training in Richmond (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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Upcoming Courses

Introduction to TinyML

2025-06-23 09:30
14 hours
Richmond
CA$ 3,514 (Online)
CA$ 4,514 (Classroom)

TinyML for IoT Applications

2025-07-07 09:30
21 hours
Richmond
CA$ 5,270 (Online)
CA$ 6,770 (Classroom)

Introduction to TinyML

2025-07-21 09:30
14 hours
Richmond
CA$ 3,514 (Online)
CA$ 4,514 (Classroom)

TinyML: Running AI on Ultra-Low-Power Edge Devices

2025-08-04 09:30
21 hours
Richmond
CA$ 5,270 (Online)
CA$ 6,770 (Classroom)

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TinyML Consulting
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