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Course Outline

Overview of Edge AI and Nano Banana

  • Distinct features of edge-AI workloads
  • Understanding Nano Banana's architecture and features
  • Comparing edge versus cloud deployment strategies

Readying Models for Edge Deployment

  • Selecting models and establishing baseline metrics
  • Addressing dependency and compatibility issues
  • Exporting models for further refinement

Advanced Model Compression Techniques

  • Pruning methods and structural sparsity
  • Techniques for weight sharing and parameter reduction
  • Measuring the impact of compression

Quantization for Enhanced Edge Performance

  • Post-training quantization methodologies
  • Workflows for quantization-aware training
  • Strategies for INT8, FP16, and mixed-precision formats

Acceleration via Nano Banana

  • Leveraging Nano Banana acceleration capabilities
  • Integrating ONNX with hardware backends
  • Benchmarking accelerated inference performance

Deploying to Edge Devices

  • Embedding models into mobile or embedded applications
  • Configuring runtimes and implementing monitoring
  • Diagnosing and resolving deployment challenges

Performance Profiling and Trade-off Evaluation

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

Best Practices for Edge-AI System Maintenance

  • Version control and continuous updates
  • Managing model rollbacks and compatibility
  • Ensuring security and data integrity

Wrap-up and Future Steps

Requirements

  • A solid grasp of machine learning workflows
  • Hands-on experience with Python-based model development
  • Familiarity with various neural network architectures

Target Audience

  • ML Engineers
  • Data Scientists
  • MLOps Practitioners
 14 Hours

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