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Duration 14 hours
Course Outline
Introduction to Edge AI and Model Refinement
- Grasping the concepts of edge computing and AI workloads
- Balancing act: performance against resource limitations
- Overview of optimization strategies for models
Selecting Models and Pre-training Phases
- Identifying lightweight architectures (e.g., MobileNet, TinyML, SqueezeNet)
- Recognizing model structures ideal for edge devices
- Leveraging pre-trained models as a foundational base
Fine-Tuning Processes and Transfer Learning
- Core principles of transfer learning
- Adjusting models to fit proprietary datasets
- Applying practical fine-tuning workflows
Quantization of Models
- Techniques for post-training quantization
- Quantization-aware training methods
- Assessing results and understanding trade-offs
Pruning Models and Compression Techniques
- Pruning approaches (structured versus unstructured)
- Compression methods and weight sharing
- Benchmarking the performance of compressed models
Deployment Frameworks and Instrumentation
- Utilizing TensorFlow Lite, PyTorch Mobile, and ONNX
- Ensuring edge hardware compatibility and managing runtime environments
- Toolchains for cross-platform implementation
Practical Deployment Exercises
- Implementing on Raspberry Pi, Jetson Nano, and mobile hardware
- Conducting profiling and benchmarking tests
- Resolving common deployment challenges
Recap and Future Directions
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python and deep learning frameworks
- Awareness of embedded system limitations and edge device constraints
Target Audience
- Embedded AI developers
- Edge computing specialists
- Machine learning engineers specializing in edge integration