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Course Outline

Introduction to Edge AI and Nano Banana

  • Core features of edge-AI workloads
  • Architecture and capabilities of Nano Banana
  • Contrasting edge versus cloud deployment strategies

Preparing Models for Edge Deployment

  • Selecting models and performing baseline evaluations
  • Addressing dependencies and compatibility requirements
  • Exporting models for subsequent optimization

Model Compression Techniques

  • Pruning methods and structural sparsity
  • Weight sharing and reducing parameters
  • Assessing the effects of compression

Quantization for Edge Performance

  • Post-training quantization techniques
  • Workflows for quantization-aware training
  • Approaches involving INT8, FP16, and mixed precision

Acceleration with Nano Banana

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

Deployment to Edge Devices

  • Incorporating models into embedded or mobile applications
  • Configuring and monitoring the runtime environment
  • Resolving common deployment challenges

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

  • Managing versioning and continuous updates
  • Handling model rollback and compatibility
  • Addressing security and integrity concerns

Summary and Next Steps

Requirements

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

Target Audience

  • ML engineers
  • Data scientists
  • MLOps practitioners
 14 Hours

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