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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining traits of edge-AI tasks
- Nano Banana framework and its core features
- Contrasting edge versus cloud deployment approaches
Model Readiness for Edge Implementation
- Selecting models and establishing performance baselines
- Addressing dependencies and compatibility requirements
- Exporting models for subsequent optimization steps
Techniques for Model Compression
- Pruning methodologies and structural sparsity
- Sharing weights and reducing parameter counts
- Assessing the effects of compression
Quantization to Boost Edge Efficiency
- Post-training quantization techniques
- Workflows for quantization-aware training
- Strategies involving INT8, FP16, and mixed precision
Speeding Up Performance with Nano Banana
- Leveraging Nano Banana acceleration tools
- Connecting ONNX standards with hardware backends
- Measuring the speed of accelerated inference
Implementing Models on Edge Hardware
- Embedding models within mobile or embedded apps
- Setting up runtime environments and monitoring systems
- Resolving common deployment challenges
Performance Analysis and Balancing Trade-offs
- Managing latency, data throughput, and heat limits
- Navigating the accuracy versus performance dilemma
- Applying iterative refinement strategies
Best Practices for Sustaining Edge-AI Systems
- Managing versions and ongoing updates
- Handling rollbacks and maintaining compatibility
- Addressing security and data integrity needs
Recap and Future Directions
Requirements
- Core understanding of machine learning pipelines
- Hands-on experience building models in Python
- Knowledge of neural network structures
Target Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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