Edge AI with TensorFlow Lite Training Course
TensorFlow Lite offers a streamlined version of TensorFlow, specifically engineered for mobile and embedded devices. This course on Edge AI with TensorFlow Lite concentrates on applying TensorFlow Lite to create and roll out Edge AI models. It explores the unique tools and methods associated with TensorFlow Lite, equipping learners with hands-on expertise to construct high-performance AI models for edge hardware.
This instructor-led live session, available either online or onsite, is tailored for intermediate developers, data scientists, and AI professionals looking to apply TensorFlow Lite in Edge AI contexts.
Upon completing this training, participants will be able to:
- Grasp the core principles of TensorFlow Lite and its function within Edge AI.
- Create and refine AI models utilizing TensorFlow Lite.
- Deploy TensorFlow Lite models across a range of edge devices.
- Employ specific tools and techniques for model conversion and optimization.
- Build functional Edge AI applications using TensorFlow Lite.
Course Format
- Engaging lectures and discussions.
- Ample exercises and practical practice.
- Real-world implementation within a live laboratory setting.
Options for Customizing the Course
- To arrange a tailored training session for this course, please get in touch with us.
Course Outline
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
Requirements
- A foundational understanding of AI and machine learning principles
- Prior experience working with TensorFlow
- Fundamental programming capabilities (Python is recommended)
Target Audience
- Developers
- Data scientists
- AI practitioners
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Edge AI with TensorFlow Lite Training Course - Enquiry
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Course - Advanced Edge AI Techniques
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