Speaker
Description
This hands-on workshop dives into TensorFlow Lite, a powerful toolkit for deploying machine learning models on devices with limited resources. We'll explore its capabilities for on-device machine learning specifically on Ubuntu Core, a lightweight Ubuntu variant optimized for Internet of Things (IoT) applications.
Throughout the session, you'll gain practical experience:
* Grasp the fundamentals of TensorFlow Lite and its role in enabling on-device ML.
* Learn how to set up the development environment for deploying TensorFlow Lite models on Ubuntu Core devices.
* Explore techniques for converting and optimizing existing TensorFlow models for deployment on resource-constrained devices.
* Engage in a practical coding session where you'll build a simple application that leverages TensorFlow Lite on Ubuntu Core to perform on-device machine learning tasks.
What audience can learn
Key Takeaways:
* Attendees will acquire the skills to convert and deploy TensorFlow models for on-device execution on Ubuntu Core.
* Gain hands-on experience with TensorFlow Lite development tools and libraries.
* Understand the benefits and considerations of on-device machine learning - TFLite
Biography
Google Developer Expert in ML | TFUG Durg Organizer | WG & SIG Member of TensorFlow.js | Work @CodeLabs | Ex-Developer Advocate at Postman and Turing
Things to know or prepare for this session
This workshop is ideal for developers interested in implementing machine learning models directly on IoT devices using the Ubuntu Core and TensorFlow Lite combination.
Summary
- Understanding TensorFlow Lite
- Setting Up the Ubuntu Environment
- Model Conversion and Optimization
| Difficulty level | Intermediate |
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