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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining key characteristics of edge-AI workloads
- Overview of Nano Banana architecture and capabilities
- Edge vs. cloud deployment strategies: A comparative view
Preparing Models for Edge Deployment
- Selecting models and establishing baseline evaluations
- Assessing dependencies and compatibility requirements
- Exporting models for advanced optimization
Model Compression Techniques
- Pruning strategies and structural sparsity methods
- Weight sharing and reducing parameter count
- Assessing the impact of compression on performance
Quantization for Edge Performance
- Methods for post-training quantization
- Workflows for quantization-aware training
- Approaches involving INT8, FP16, and mixed-precision
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking the performance of accelerated inference
Deployment to Edge Devices
- Embedding models into mobile or embedded applications
- Configuring and monitoring runtime environments
- Resolving common deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal limits
- Navigating the accuracy versus performance trade-off
- Strategies for iterative optimization
Best Practices for Maintaining Edge-AI Systems
- Managing version control and continuous updates
- Handling model rollbacks and compatibility
- Considerations for security and data integrity
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Experience in developing models using Python
- Knowledge of neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Practitioners
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