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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining key characteristics of edge-AI workloads
- Examining Nano Banana’s architecture and core capabilities
- Analyzing differences between edge and cloud deployment strategies
Preparation for Edge Model Deployment
- Selecting models and establishing baseline evaluations
- Addressing dependency and compatibility requirements
- Exporting models for subsequent optimization phases
Advanced Model Compression Methods
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing and parameter reduction techniques
- Assessing the impact of compression on model performance
Quantization Strategies for Edge Efficiency
- Exploring post-training quantization methods
- Implementing quantization-aware training workflows
- Employing INT8, FP16, and mixed-precision techniques
Acceleration via Nano Banana
- Leveraging Nano Banana accelerators for enhanced performance
- Integrating ONNX standards and hardware backends
- Benchmarking accelerated inference outcomes
Deploying to Edge Devices
- Embedding models into mobile or embedded applications
- Configuring and monitoring runtime environments
- Diagnosing and resolving common deployment challenges
Performance Analysis and Trade-off Evaluation
- Managing latency, throughput, and thermal constraints
- Navigating the accuracy versus performance trade-off
- Developing iterative optimization strategies
Best Practices for Edge-AI System Maintenance
- Implementing versioning and continuous update protocols
- Managing model rollbacks and compatibility
- Addressing security and integrity requirements
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python-based model development
- Knowledge of various neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Practitioners
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