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Course Outline

Foundations of Edge AI and the Nano Banana Framework

  • Essential traits of edge-AI workloads
  • Overview of Nano Banana’s architecture and features
  • Analysis of edge versus cloud deployment strategies

Preparing Models for Edge Integration

  • Selecting models and establishing performance baselines
  • Considering dependencies and system compatibility
  • Exporting models for subsequent optimization

Techniques for Model Compression

  • Structural sparsity and pruning methodologies
  • Parameter reduction via weight sharing
  • Assessing the effects of compression

Quantization Strategies for Edge Efficiency

  • Post-training quantization approaches
  • Workflows for quantization-aware training
  • Utilizing INT8, FP16, and mixed-precision methods

Accelerating Performance with Nano Banana

  • Leveraging Nano Banana accelerators
  • Integration with ONNX and specific hardware backends
  • Benchmarking the speed of accelerated inference

Deploying to Edge Hardware

  • Embedding models into mobile or embedded applications
  • Configuring and monitoring runtime performance
  • Resolving common deployment challenges

Profiling Performance and Analyzing Trade-offs

  • Managing latency, throughput, and thermal limits
  • Balancing accuracy against performance demands
  • Applying iterative optimization techniques

Best Practices for Sustaining Edge-AI Systems

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

Conclusion and Future Pathways

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python-based model development
  • Knowledge of neural network structures

Target Audience

  • Machine Learning Engineers
  • Data Scientists
  • MLOps Specialists
 14 Hours

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