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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Core attributes of edge-AI tasks
- Nano Banana structural design and features
- Evaluating edge versus cloud deployment approaches
Readying Models for Edge Implementation
- Selecting models and establishing performance baselines
- Assessing dependencies and compatibility requirements
- Exporting models to prepare for optimization
Strategies for Model Compression
- Pruning techniques and structural sparsity
- Parameter sharing and reduction methods
- Assessing the impact of compression
Quantization for Optimal Edge Performance
- 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 tools
- Connecting with ONNX and hardware-specific backends
- Testing the speed of accelerated inference
Rolling Out to Edge Devices
- Incorporating models into embedded or mobile applications
- Configuring runtimes and monitoring systems
- Resolving common deployment challenges
Analyzing Performance and Trade-offs
- Managing latency, throughput, and heat limits
- Balancing precision against performance gains
- Applying iterative optimization methods
Maintaining Edge-AI Systems: Best Practices
- Managing versions and ongoing updates
- Handling model rollbacks and compatibility
- Addressing security and data integrity
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning processes
- Practical experience with Python-based model creation
- Knowledge of neural network structures
Intended Audience
- ML engineers
- Data scientists
- MLOps specialists
Testimonials (1)
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