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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the key attributes of edge-AI workloads
- Examining the architecture and core capabilities of Nano Banana
- An analysis of edge versus cloud deployment methodologies
Readying Models for Edge Environments
- Selecting appropriate models and establishing baseline evaluations
- Navigating dependency and compatibility requirements
- Preparing model exports for subsequent optimization phases
Advanced Model Compression Methods
- Applying pruning strategies and achieving structural sparsity
- Utilizing weight sharing to reduce parameter counts
- Assessing the effects of compression on model performance
Quantization for Enhanced Edge Performance
- Implementing post-training quantization techniques
- Managing quantization-aware training processes
- Working with INT8, FP16, and mixed-precision strategies
Leveraging Acceleration with Nano Banana
- Utilizing Nano Banana-specific accelerators
- Integrating ONNX formats with hardware backends
- Conducting benchmarks for accelerated inference speeds
Executing Deployment on Edge Devices
- Incorporating models into embedded systems or mobile applications
- Configuring runtimes and establishing monitoring protocols
- Resolving common deployment challenges
Performance Analysis and Strategic Trade-offs
- Addressing latency, throughput, and thermal limitations
- Navigating the trade-offs between accuracy and performance
- Developing iterative optimization strategies
Sustaining Edge-AI System Best Practices
- Managing version control and continuous model updates
- Overseeing model rollbacks and compatibility management
- Incorporating security and data integrity measures
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based model development
- Knowledge of various neural network architectures
Target Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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