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Course Outline

Introduction to Edge AI and Nano Banana

  • Core characteristics of edge-AI workloads
  • Overview of Nano Banana’s architecture and capabilities
  • Comparison of edge versus cloud deployment strategies

Preparing Models for Edge Deployment

  • Model selection and baseline evaluation
  • Considerations for dependencies and compatibility
  • Exporting models to enable further optimization

Model Compression Techniques

  • Pruning strategies and structural sparsity
  • Weight sharing and parameter reduction
  • Assessing the impact of compression

Quantization for Edge Performance

  • Post-training quantization methods
  • Quantization-aware training workflows
  • Exploring INT8, FP16, and mixed-precision approaches

Acceleration with Nano Banana

  • Utilizing Nano Banana accelerators
  • Integrating ONNX and hardware backends
  • Benchmarking accelerated inference

Deployment to Edge Devices

  • Integrating models into embedded or mobile applications
  • Runtime configuration and monitoring
  • Resolving common deployment issues

Performance Profiling and Trade-off Analysis

  • Managing latency, throughput, and thermal constraints
  • Balancing accuracy against performance
  • Employing iterative optimization strategies

Best Practices for Maintaining Edge-AI Systems

  • Versioning and continuous update processes
  • Managing model rollbacks and compatibility
  • Addressing security and integrity considerations

Summary and Next Steps

Requirements

  • A solid understanding of machine learning workflows
  • Experience in Python-based model development
  • Familiarity with neural network architectures

Target Audience

  • ML engineers
  • Data scientists
  • MLOps practitioners
 14 Hours

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