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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Key characteristics of edge-AI workloads.
- Nano Banana architecture and capabilities.
- Comparison of edge vs. cloud deployment strategies.
Preparing Models for Edge Deployment
- Model selection and baseline evaluation.
- Considerations for dependencies and compatibility.
- Exporting models for subsequent optimization.
Model Compression Techniques
- Pruning strategies and structural sparsity.
- Weight sharing and parameter reduction.
- Evaluating the impact of compression.
Quantization for Edge Performance
- Post-training quantization methods.
- Quantization-aware training workflows.
- INT8, FP16, and mixed-precision approaches.
Acceleration with Nano Banana
- Utilizing Nano Banana accelerators.
- Integration of ONNX and hardware backends.
- Benchmarking accelerated inference.
Deployment to Edge Devices
- Integrating models into embedded or mobile applications.
- Runtime configuration and monitoring.
- Troubleshooting deployment challenges.
Performance Profiling and Trade-off Analysis
- Latency, throughput, and thermal constraints.
- Accuracy vs. performance trade-offs.
- Iterative optimization strategies.
Best Practices for Maintaining Edge-AI Systems
- Versioning and continuous updates.
- Model rollback and compatibility management.
- Security and integrity considerations.
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows.
- Experience with model development using Python.
- Familiarity with neural network architectures.
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
Testimonials (1)
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