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Course Outline

Overview of Edge AI and the Nano Banana Framework

  • Defining the unique demands of edge-AI tasks
  • Exploring Nano Banana's design and functional capabilities
  • Contrasting edge-based versus cloud-based deployment methods

Readying Models for Edge Integration

  • Selecting appropriate models and establishing performance baselines
  • Addressing dependencies and system compatibility
  • Exporting models to prepare for advanced optimization

Strategies for Model Compression

  • Applying pruning tactics and achieving structural sparsity
  • Utilizing weight sharing and minimizing parameters
  • Assessing the impact of compression techniques

Quantization to Boost Edge Efficiency

  • Employing post-training quantization methods
  • Managing quantization-aware training processes
  • Exploring INT8, FP16, and mixed-precision strategies

Speeding Up Inference with Nano Banana

  • Leveraging Nano Banana's acceleration features
  • Connecting ONNX formats with specific hardware backends
  • Testing the performance of accelerated inference

Integrating Models into Edge Devices

  • Embedding models within mobile or embedded software
  • Configuring runtime settings and overseeing operations
  • Resolving common deployment challenges

Performance Analysis and Balancing Trade-offs

  • Managing latency, data throughput, and thermal limits
  • Balancing model accuracy against operational speed
  • Implementing iterative refinement techniques

Best Practices for Sustaining Edge-AI Systems

  • Handling version control and ongoing updates
  • Managing rollbacks and ensuring compatibility
  • Addressing security and data integrity concerns

Conclusion and Future Directions

Requirements

  • Proficiency in machine learning processes
  • Practical experience with Python-based model creation
  • Knowledge of neural network designs

Target Audience

  • ML Engineers
  • Data Scientists
  • MLOps Specialists
 14 Hours

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