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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the core characteristics of edge-AI workloads
- Examining Nano Banana’s architecture and core capabilities
- Contrasting edge-based versus cloud-based deployment strategies
Readying Models for Edge Environments
- Selecting models and conducting baseline assessments
- Addressing dependency and compatibility requirements
- Exporting models to prepare for deeper optimization
Advanced Model Compression Strategies
- Exploring pruning strategies and structural sparsity
- Applying weight sharing and parameter reduction techniques
- Assessing the impact of compression on model performance
Quantization for Enhanced Edge Performance
- Applying post-training quantization methods
- Integrating quantization-aware training workflows
- Leveraging INT8, FP16, and mixed-precision methodologies
Acceleration via Nano Banana
- Utilizing Nano Banana hardware accelerators
- Integrating ONNX and compatible hardware backends
- Conducting benchmarks for accelerated inference
Deploying to Edge Devices
- Embedding models within mobile or embedded applications
- Configuring runtimes and establishing monitoring systems
- Resolving common deployment challenges
Performance Profiling and Strategic Trade-offs
- Managing latency, throughput, and thermal limits
- Navigating the trade-offs between accuracy and performance
- Implementing iterative optimization cycles
Best Practices for Sustaining Edge-AI Systems
- Managing versioning and continuous updates
- Handling model rollbacks and compatibility issues
- Ensuring system security and integrity
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based model development
- Adeptness with neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
Testimonials (1)
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