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Course Outline

Introduction to Edge AI and Nano Banana

  • Distinct attributes of edge AI workloads
  • Overview of Nano Banana’s design and features
  • Contrasting edge-based versus cloud-based deployment approaches

Model Preparation for Edge Implementation

  • Selecting appropriate models and establishing baseline performance metrics
  • Addressing dependency and compatibility requirements
  • Exporting models to enable further refinement

Advanced Model Compression Methods

  • Strategies for pruning and introducing structural sparsity
  • Utilizing weight sharing to reduce parameter count
  • Assessing the effects of compression on model quality

Quantization Strategies for Edge Efficiency

  • Techniques for post-training quantization
  • Workflows involving quantization-aware training
  • Application of INT8, FP16, and mixed-precision methods

Performance Acceleration using Nano Banana

  • Leveraging Nano Banana’s acceleration capabilities
  • Integrating ONNX formats with hardware-specific backends
  • Conducting benchmarks on accelerated inference processes

Implementing Models on Edge Hardware

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

Performance Analysis and Trade-off Management

  • Managing constraints related to latency, throughput, and heat
  • Balancing accuracy against performance metrics
  • Employing iterative optimization techniques

Best Practices for Sustaining Edge AI Systems

  • Managing version control and ongoing updates
  • Handling model rollbacks and compatibility issues
  • Addressing security and system integrity requirements

Conclusion and Recommended Next Steps

Requirements

  • A solid grasp of machine learning workflows.
  • Practical experience in developing models using Python.
  • Knowledge of various neural network structures.

Target Audience

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

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