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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining the 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
- Assessing dependency and compatibility requirements
- Exporting models to enable further optimisation
Model Compression Techniques
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing for parameter reduction
- Assessing the impact of compression on model performance
Quantization for Edge Performance
- Implementing post-training quantization methods
- Understanding quantization-aware training workflows
- Exploring INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Running benchmarks for accelerated inference
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Configuring and monitoring runtime environments
- Diagnosing and resolving deployment issues
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal constraints
- Navigating the balance between accuracy and performance
- Developing iterative optimisation 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
- Working knowledge of neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
Testimonials (1)
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