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Course Outline

Foundations of Edge AI and Nano Banana

  • Defining the key attributes of edge-AI workloads
  • Exploring Nano Banana’s architecture and core capabilities
  • Analyzing the differences between edge and cloud deployment strategies

Ready-Making Models for Edge Environments

  • Selecting appropriate models and establishing performance baselines
  • Navigating dependency and compatibility requirements
  • Preparing model exports for subsequent optimization

Advanced Model Compression Methods

  • Applying pruning strategies to achieve structural sparsity
  • Utilizing weight sharing to reduce parameter counts
  • Assessing the impact of compression on model performance

Leveraging Quantization for Edge Efficiency

  • Implementing post-training quantization techniques
  • Managing quantization-aware training workflows
  • Working with INT8, FP16, and mixed-precision formats

Performance Acceleration via Nano Banana

  • Harnessing Nano Banana accelerators for speed
  • Integrating ONNX models with specific hardware backends
  • Conducting benchmarks for accelerated inference

Deploying to Edge Devices

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

Performance Profiling and Strategic Trade-offs

  • Balancing latency, throughput, and thermal limits
  • Navigating the accuracy versus performance equation
  • Adopting iterative optimization approaches

Sustaining Edge-AI Systems: Best Practices

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

Conclusion and Future Pathways

Requirements

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

Target Audience

  • ML engineers
  • Data scientists
  • MLOps practitioners
 14 Hours

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