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Course Outline

Overview of Edge AI and Nano Banana

  • Distinct features of edge AI workloads
  • Nano Banana framework architecture and core capabilities
  • Contrasting edge versus cloud deployment models

Readying Models for Edge Integration

  • Selecting appropriate models and establishing performance baselines
  • Addressing dependencies and compatibility requirements
  • Exporting models to facilitate further optimization

Techniques for Model Compression

  • Pruning methodologies and structural sparsity
  • Weight sharing techniques and parameter minimization
  • Assessing the effects of compression on model quality

Quantization Strategies for Edge Efficiency

  • Post-training quantization procedures
  • Workflows for quantization-aware training
  • Utilizing INT8, FP16, and mixed-precision methods

Performance Acceleration via Nano Banana

  • Leveraging Nano Banana acceleration features
  • Integration with ONNX and hardware-specific backends
  • Conducting benchmarks for accelerated inference

Deploying to Edge Hardware

  • Incorporating models into embedded or mobile applications
  • Configuring runtimes and setting up monitoring
  • Resolving common deployment challenges

Performance Profiling and Balancing Trade-offs

  • Managing latency, throughput, and thermal limitations
  • Navigating the balance between accuracy and performance
  • Applying iterative optimization techniques

Best Practices for Sustaining Edge AI Systems

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

Wrap-up and Future Directions

Requirements

  • Foundational knowledge of machine learning processes
  • Proficiency in Python-based model development
  • Working understanding of neural network designs

Target Audience

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

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