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Course Outline

Foundations of Edge AI and Nano Banana

  • Defining the key attributes of edge-AI workloads
  • Overview of Nano Banana's architecture and capabilities
  • A comparative analysis of edge versus cloud deployment strategies

Readying Models for Edge Deployment

  • Selecting appropriate models and establishing baseline metrics
  • Addressing dependency and compatibility requirements
  • Exporting models to prepare them for optimization

Techniques for Model Compression

  • Exploring pruning strategies and structural sparsity
  • Applying weight sharing and parameter reduction
  • Assessing the impact of compression on model quality

Quantization for Enhanced Edge Performance

  • Methods for post-training quantization
  • Workflows for quantization-aware training
  • Utilizing INT8, FP16, and mixed-precision techniques

Leveraging Nano Banana for Acceleration

  • Utilizing Nano Banana accelerators effectively
  • Integrating ONNX standards with hardware backends
  • Benchmarking the performance of accelerated inference

Deploying to Edge Devices

  • Integrating models into embedded or mobile applications
  • Configuring and monitoring runtime behavior
  • Resolving common deployment challenges

Performance Profiling and Trade-off Analysis

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

Best Practices for Sustainig Edge-AI Systems

  • Implementing versioning and continuous update processes
  • Managing model rollbacks and compatibility
  • Addressing security and data integrity concerns

Conclusion and Recommended Next Steps

Requirements

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

Target Audience

  • ML Engineers
  • Data Scientists
  • MLOps Practitioners
 14 Hours

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