Fundamentals & Applications of TinyML
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Training TypeLive Training
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CategoryMachine Learning
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Duration3 Hours
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Rating4.9/5
Course Introduction
About the Course
TinyML is revolutionizing the world of embedded systems by enabling machine learning (ML) on resource-constrained devices like microcontrollers and sensors. This training will cover the fundamental concepts behind TinyML, introduce key tools and frameworks, and demonstrate how to develop and deploy TinyML models for IoT applications. Attendees will gain hands-on experience in building efficient models for real-world use cases such as health monitoring, smart agriculture, and predictive maintenance.
Course Objective
Understand the key concepts and architecture of TinyML.
Learn how to train machine learning models for embedded devices.
Gain practical experience with tools and frameworks like TensorFlow Lite for Microcontrollers.
Understand the trade-offs between accuracy, performance, and resource usage in TinyML.
Be able to deploy TinyML models on various microcontroller platforms.
Learn about real-world applications of TinyML in fields such as IoT, healthcare, and industrial automation.
Who is the Target Audience?
Engineers and developers working on embedded systems and IoT solutions.
Data scientists and machine learning practitioners interested in applying ML to edge devices.
Hobbyists and enthusiasts looking to explore TinyML for personal projects.
Academics or researchers in fields related to embedded computing and AI.
Basic Knowledge
Basic understanding of machine learning concepts (classification, regression, etc.).
Familiarity with embedded systems or microcontrollers (e.g., Arduino, Raspberry Pi, or similar).
Basic programming skills, ideally in Python or C/C++.
Available Batches
| 20 Feb 2025 | Thu ( 1 Day ) | 02:00 PM - 05:00 PM (Eastern Time) |
| 19 Mar 2025 | Wed ( 1 Day ) | 02:00 PM - 05:00 PM (Eastern Time) |
| 23 Apr 2025 | Wed ( 1 Day ) | 02:00 PM - 05:00 PM (Eastern Time) |
Pricing
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What is TinyML?
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Why is TinyML important for IoT and edge computing?
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Key challenges and opportunities in TinyML.
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Overview of ML techniques relevant for TinyML.
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Supervised learning, classification, and regression.
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Key metrics for evaluating model performance.
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Overview of microcontrollers and edge devices suitable for TinyML.
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Introduction to frameworks like TensorFlow Lite for Microcontrollers and Edge Impulse.aIntroduction to frameworks like TensorFlow Lite for Microcontrollers and Edge Impulse.
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Data collection and preprocessing for TinyML applications.
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Designing and training machine learning models on constrained devices.
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Model optimization techniques (quantization, pruning, etc.).
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How to deploy models to embedded platforms.
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Running models on platforms like Arduino, Raspberry Pi, and others.
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Real-time inference with minimal power consumption.
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Case studies: Smart agriculture, healthcare monitoring, predictive maintenance, etc.
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Challenges in deploying TinyML in real-world environments.
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Future trends and potential advancements in TinyML.
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Deploying a simple TinyML model on an embedded device.
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Step-by-step walkthrough of setting up an IoT project using TinyML.
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Troubleshooting tips for common issues.
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Open floor for participant questions.