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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the core attributes of edge-AI workloads
- Overview of Nano Banana’s architecture and features
- Contrasting edge deployment with cloud-based strategies
Preparing Models for Edge Environments
- Selecting appropriate models and establishing baseline metrics
- Addressing dependency and compatibility requirements
- Exporting models to enable further optimization
Techniques for Model Compression
- Applying pruning methods and structural sparsity
- Utilizing weight sharing to reduce parameters
- Assessing the impact of compression on model performance
Quantization Strategies for Edge Efficiency
- Methods for post-training quantization
- Workflows for quantization-aware training
- Exploring INT8, FP16, and mixed-precision techniques
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana’s acceleration capabilities
- Integrating with ONNX and specific hardware backends
- Benchmarking the performance of accelerated inference
Deploying to Edge Hardware
- Incorporating models into embedded or mobile applications
- Configuring runtime settings and establishing monitoring
- Diagnosing and resolving common deployment challenges
Performance Profiling and Trade-off Evaluation
- Analyzing latency, throughput, and thermal limitations
- Reconciling accuracy with performance requirements
- Developing iterative optimization approaches
Best Practices for Sustaining Edge-AI Systems
- Managing version control and continuous updates
- Handling model rollbacks and ensuring compatibility
- Addressing security and data integrity concerns
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience developing models with Python
- Knowledge of various neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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