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Duration 14 hours
Course Outline
Overview of Edge AI and Nano Banana
- Essential traits of edge-AI workloads
- Nano Banana framework design and features
- Edge versus cloud deployment strategy comparison
Model Preparation for Edge Environments
- Selecting models and establishing performance baselines
- Assessing dependencies and compatibility requirements
- Exporting models to enable further optimization
Compression Methodologies for Models
- Pruning tactics and structural sparsity implementation
- Weight sharing and parameter minimization
- Assessing the impact of compression
Quantization Strategies for Edge Efficiency
- Post-training quantization techniques
- Workflows for quantization-aware training
- Application of INT8, FP16, and mixed-precision standards
Enhancing Performance with Nano Banana
- Leveraging Nano Banana acceleration features
- Integration of ONNX and hardware-specific backends
- Benchmarking inference performance after acceleration
Deploying to Edge Hardware
- Incorporating models into embedded or mobile applications
- Configuring and monitoring runtime operations
- Resolving common deployment challenges
Performance Analysis and Trade-off Evaluation
- Managing latency, throughput, and thermal limits
- Balancing accuracy against performance metrics
- Applying iterative optimization approaches
Best Practices for Edge-AI System Maintenance
- Managing versioning and continuous updates
- Handling model rollbacks and compatibility
- Addressing security and data integrity concerns
Conclusion and Future Pathways
Requirements
- Comprehensive understanding of machine learning pipelines
- Proficiency in Python-based model creation
- Knowledge of neural network structures
Target Professionals
- ML Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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