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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining features of edge-AI operations
- Nano Banana framework structure and potential
- Edge versus cloud deployment strategies: A comparative view
Readying Models for Edge Integration
- Selecting models and establishing baseline metrics
- Reviewing dependencies and compatibility requirements
- Preparing models for subsequent optimization phases
Techniques for Model Compression
- Pruning methodologies and structural sparsity
- Parameter reduction and weight sharing techniques
- Assessing the effects of compression
Quantization Strategies for Edge Efficiency
- Methods for post-training quantization
- Workflows for quantization-aware training
- Implementing INT8, FP16, and mixed-precision strategies
Speeding Up Inference with Nano Banana
- Leveraging Nano Banana acceleration features
- Connecting ONNX and hardware backend components
- Testing and measuring accelerated inference performance
Rolling Out to Edge Hardware
- Incorporating models into mobile or embedded applications
- Setting up runtime parameters and monitoring systems
- Resolving common deployment challenges
Analyzing Performance and Balancing Trade-offs
- Managing latency, throughput, and heat dissipation limits
- Weighing accuracy against performance gains
- Employing iterative improvement tactics
Best Practices for Sustaining Edge-AI Systems
- Managing version control and ongoing updates
- Handling model rollback and compatibility checks
- Addressing security and system integrity concerns
Conclusion and Future Directions
Requirements
- Comprehension of machine learning processes
- Proficiency in developing models using Python
- Knowledge of neural network structures
Intended Participants
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
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