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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
- Comparing edge versus cloud deployment strategies
Preparing Models for Edge Deployment
- Model selection and baseline evaluation
- Dependency and compatibility considerations
- Exporting models for further optimisation
Model Compression Techniques
- Pruning strategies and structural sparsity
- Weight sharing and parameter reduction
- Evaluating the impacts of compression
Quantisation for Edge Performance
- Post-training quantisation methods
- Quantisation-aware training workflows
- INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Utilising Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Runtime configuration and monitoring
- Troubleshooting deployment issues
Performance Profiling and Trade-off Analysis
- Latency, throughput, and thermal constraints
- Accuracy versus performance trade-offs
- Iterative optimisation 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 Python-based model development
- Familiarity with neural network architectures
Audience
- ML Engineers
- Data Scientists
- MLOps Practitioners
Testimonials (1)
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