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Duration 14 hours
Course Outline
Overview of Edge AI and Nano Banana
- Defining the core characteristics of edge AI workloads
- Examining Nano Banana’s architecture and core capabilities
- Contrasting edge versus cloud deployment strategies
Readying Models for Edge Deployment
- Selecting models and establishing baseline evaluations
- Addressing dependency and compatibility factors
- Exporting models to prepare for further optimization
Model Compression Strategies
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing and parameter reduction techniques
- Measuring the impact of compression on model performance
Quantization for Edge Efficiency
- Exploring post-training quantization methods
- Implementing quantization-aware training workflows
- Working with INT8, FP16, and mixed-precision approaches
Accelerating Performance with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX with hardware backends
- Benchmarking accelerated inference results
Deploying to Edge Devices
- Embedding models into mobile or embedded applications
- Managing runtime configurations and monitoring
- Resolving common deployment challenges
Performance Analysis and Trade-off Evaluation
- Analyzing latency, throughput, and thermal limits
- Balancing accuracy against performance metrics
- Adopting iterative optimization strategies
Maintaining Edge AI Systems: Best Practices
- Managing version control and continuous updates
- Handling model rollbacks and compatibility issues
- Addressing security and data integrity concerns
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python-based model development
- Knowledge of neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
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