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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining key characteristics of edge-AI workloads
- Overview of Nano Banana architecture and capabilities
- Comparing edge versus cloud deployment strategies
Preparing Models for Edge Deployment
- Model selection and establishing baseline evaluations
- Addressing dependency and compatibility considerations
- Exporting models for subsequent optimization
Model Compression Techniques
- Applying pruning strategies and structural sparsity
- Implementing weight sharing and parameter reduction
- Evaluating the impact of compression
Quantization for Edge Performance
- Using post-training quantization methods
- Managing quantization-aware training workflows
- Applying INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference performance
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Configuring and monitoring runtime environments
- Resolving common deployment issues
Performance Profiling and Trade-off Analysis
- Analyzing latency, throughput, and thermal constraints
- Weighing accuracy against performance trade-offs
- Employing iterative optimization strategies
Best Practices for Maintaining Edge-AI Systems
- Managing versioning and continuous updates
- Handling model rollback and compatibility
- Addressing security and integrity considerations
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based model development
- Knowledge of neural network architectures
Intended Audience
- ML Engineers
- Data Scientists
- MLOps Practitioners
Testimonials (1)
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