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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining attributes of edge-AI tasks
- Nano Banana structural design and features
- Contrasting edge versus cloud deployment methods
Readying Models for Edge Implementation
- Choosing models and establishing baseline metrics
- Assessing dependencies and compatibility needs
- Exporting models for advanced optimization
Techniques for Model Compression
- Pruning methods and structural sparsity
- Parameter minimization through weight sharing
- Assessing the effects of compression
Quantization for Enhanced Edge Performance
- Post-training quantization techniques
- Quantization-aware training processes
- Approaches involving INT8, FP16, and mixed precision
Performance Acceleration via Nano Banana
- Leveraging Nano Banana accelerators
- Integration with ONNX and hardware backends
- Testing the speed of accelerated inference
Implementing on Edge Devices
- Embedding models within mobile or embedded apps
- Configuring and monitoring runtime performance
- Resolving deployment challenges
Performance Analysis and Trade-off Assessment
- Constraints related to latency, throughput, and heat
- Balancing accuracy against performance
- Strategies for iterative refinement
Best Practices for Sustaining Edge-AI Systems
- Version control and ongoing updates
- Managing model rollbacks and compatibility
- Considerations for security and integrity
Wrap-up and Future Steps
Requirements
- Comprehensive grasp of machine learning pipelines
- Proficiency in developing models using Python
- Knowledge of neural network frameworks
Target Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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