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Course Outline

Overview of Edge AI and the Nano Banana Ecosystem

  • Defining the core attributes of edge-AI workloads
  • Exploring Nano Banana’s architecture and functional capabilities
  • Contrasting edge-based versus cloud-based deployment strategies

Getting Models Ready for Edge Implementation

  • Selecting models and establishing baseline performance metrics
  • Addressing dependencies and compatibility requirements
  • Exporting models to enable further optimization

Strategies for Model Compression

  • Applying pruning techniques and structural sparsity
  • Leveraging weight sharing to reduce parameter count
  • Assessing the impact of compression on model behavior

Quantization Methods for Edge Efficiency

  • Techniques for post-training quantization
  • Workflows for quantization-aware training
  • Utilizing INT8, FP16, and mixed-precision formats

Performance Acceleration via Nano Banana

  • Leveraging Nano Banana’s acceleration features
  • Incorporating ONNX and various hardware backends
  • Conducting benchmarks on accelerated inference tasks

Rolling Out to Edge Hardware

  • Embedding models into mobile or embedded applications
  • Configuring runtime settings and monitoring performance
  • Diagnosing and resolving deployment challenges

Performance Analysis and Trade-off Evaluation

  • Managing latency, throughput, and thermal limitations
  • Balancing accuracy against performance metrics
  • Employing iterative optimization techniques

Best Practices for Sustaining Edge-AI Systems

  • Implementing version control and continuous deployment
  • Managing model rollbacks and ensuring compatibility
  • Addressing security and data integrity concerns

Conclusion and Future Directions

Requirements

  • A solid grasp of machine learning pipelines
  • Proficiency in developing models using Python
  • Knowledge of neural network structures

Intended Participants

  • ML Engineers
  • Data Scientists
  • MLOps Specialists
 14 Hours

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