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Course Outline
Foundations of Edge AI and Nano Banana
- Defining traits of edge-AI tasks
- Nano Banana’s design and functional scope
- Evaluating edge versus cloud deployment tactics
Ready Models for Edge Implementation
- Choosing models and establishing performance baselines
- Addressing dependencies and system compatibility
- Preparing models for advanced optimization
Strategies for Model Compression
- Pruning approaches and structural sparsity
- Techniques for weight sharing and parameter minimization
- Assessing the effect of compression
Quantization for Enhanced Edge Performance
- Methods for post-training quantization
- Workflows for quantization-aware training
- INT8, FP16, and mixed-precision strategies
Performance Acceleration via Nano Banana
- Utilizing Nano Banana’s acceleration capabilities
- Incorporating ONNX and hardware-specific backends
- Testing the speed of accelerated inference
Implementing Models on Edge Hardware
- Embedding models into mobile or embedded applications
- Setting up runtime environments and monitoring systems
- Resolving common deployment challenges
Analyzing Performance and Trade-offs
- Managing latency, data throughput, and thermal limits
- Balancing accuracy against operational speed
- Applying iterative refinement methods
Best Practices for Sustaining Edge-AI Systems
- Managing version control and ongoing updates
- Handling rollbacks and ensuring compatibility
- Addressing security and system integrity
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning processes
- Practical experience in developing models with Python
- Knowledge of neural network structures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
14 Hours
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