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Course Outline

Foundations of Edge AI and an Introduction to Nano Banana

  • Defining the core attributes of edge-AI tasks
  • Exploring Nano Banana's architecture and core features
  • Contrasting deployment strategies: edge versus cloud

Readying Models for Edge Integration

  • Selecting appropriate models and establishing performance baselines
  • Assessing dependencies and system compatibility
  • Exporting models to prepare for subsequent optimization

Techniques for Model Compression

  • Applying pruning methods and structural sparsity
  • Utilizing weight sharing to reduce parameter counts
  • Measuring the effects of compression on model quality

Leveraging Quantization for Edge Efficiency

  • Methods for post-training quantization
  • Workflows for quantization-aware training
  • Implementation of INT8, FP16, and mixed-precision strategies

Enhancing Speed with Nano Banana

  • Utilizing Nano Banana acceleration tools
  • Connecting ONNX formats with specific hardware backends
  • Testing and measuring accelerated inference performance

Deploying to Edge Hardware

  • Embedding models into mobile or embedded systems
  • Configuring runtimes and implementing monitoring solutions
  • Resolving common deployment challenges

Analyzing Performance and Managing Trade-offs

  • Navigating latency, throughput, and thermal limits
  • Balancing accuracy against speed and resource usage
  • Employing iterative strategies for continuous optimization

Best Practices for Sustaining Edge-AI Systems

  • Managing version control and continuous updates
  • Handling model rollbacks and ensuring compatibility
  • Addressing security and data integrity requirements

Recap and Future Directions

Requirements

  • A solid grasp of machine learning pipelines
  • Hands-on experience developing models with Python
  • Knowledge of common neural network architectures

Target Audience

  • Machine Learning Engineers
  • Data Scientists
  • MLOps Specialists
 14 Hours

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