Edge AI with TensorFlow Lite

• An understanding of AI and machine learning concepts
• Experience with TensorFlow
• Basic programming skills (Python recommended)

Audience

• Developers
• Data scientists
• AI practitioners

TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. Edge AI with TensorFlow Lite focuses on utilizing TensorFlow Lite for developing and deploying Edge AI models. This course covers the tools and techniques specific to TensorFlow Lite, providing practical knowledge for building efficient AI models for edge devices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to TensorFlow Lite

• Overview of TensorFlow Lite and its architecture
• Comparison with TensorFlow and other edge AI frameworks
• Benefits and challenges of using TensorFlow Lite for Edge AI
• Case studies of TensorFlow Lite in Edge AI applications

Setting Up the TensorFlow Lite Environment

• Installing TensorFlow Lite and its dependencies
• Configuring the development environment
• Introduction to TensorFlow Lite tools and libraries
• Hands-on exercises for environment setup

Developing AI Models with TensorFlow Lite

• Designing and training AI models for edge deployment
• Converting TensorFlow models to TensorFlow Lite format
• Optimizing models for performance and efficiency
• Hands-on exercises for model development and conversion

Deploying TensorFlow Lite Models

• Deploying models on various edge devices (e.g., smartphones, microcontrollers)
• Running inferences on edge devices
• Troubleshooting deployment issues
• Hands-on exercises for model deployment

Tools and Techniques for Model Optimization

• Quantization and its benefits
• Pruning and model compression techniques
• Utilizing TensorFlow Lite's optimization tools
• Hands-on exercises for model optimization

Building Practical Edge AI Applications

• Developing real-world Edge AI applications using TensorFlow Lite
• Integrating TensorFlow Lite models with other systems and applications
• Case studies of successful Edge AI projects
• Hands-on project for building a practical Edge AI application

Summary and Next Steps