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Course Outline
Introduction to Edge AI and Nano Banana
- Defining key characteristics of edge-AI workloads
- Examining Nano Banana’s architecture and core capabilities
- Contrasting edge versus cloud deployment strategies
Preparing Models for Edge Deployment
- Selecting models and establishing baseline evaluations
- Addressing dependency and compatibility issues
- Exporting models to facilitate further optimization
Model Compression Techniques
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing and parameter reduction
- Assessing the impact of compression methods
Quantization for Edge Performance
- Implementing post-training quantization methods
- Following quantization-aware training workflows
- Exploring INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference performance
Deployment to Edge Devices
- Embedding models into mobile or embedded applications
- Configuring runtimes and setting up monitoring
- Resolving common deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal limits
- Navigating the accuracy-versus-performance balance
- Employing iterative optimization strategies
Best Practices for Maintaining Edge-AI Systems
- Handling versioning and continuous updates
- Managing model rollbacks and compatibility
- Ensuring security and system integrity
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based model development
- Knowledge of various neural network architectures
Intended Audience
- ML engineers
- Data scientists
- MLOps practitioners
14 Hours
Testimonials (1)
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