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Course Outline

Introduction to Edge AI and Nano Banana

  • Defining the key characteristics of edge AI workloads
  • Exploring Nano Banana architecture and capabilities
  • Contrasting edge versus cloud deployment strategies

Preparing Models for Edge Deployment

  • Model selection and baseline performance evaluation
  • Considering dependencies and compatibility requirements
  • Exporting models for subsequent optimisation steps

Model Compression Techniques

  • Applying pruning strategies and structural sparsity
  • Utilising weight sharing and parameter reduction
  • Assessing the impact of compression on performance

Quantisation for Edge Performance

  • Implementing post-training quantisation methods
  • Integrating quantisation-aware training workflows
  • Utilising INT8, FP16, and mixed-precision approaches

Acceleration with Nano Banana

  • Leveraging Nano Banana accelerators
  • Integrating ONNX and specific hardware backends
  • Benchmarking accelerated inference results

Deployment to Edge Devices

  • Integrating models into embedded or mobile applications
  • Configuring runtime environments and monitoring
  • Diagnosing and troubleshooting deployment challenges

Performance Profiling and Trade-off Analysis

  • Managing latency, throughput, and thermal constraints
  • Balancing accuracy against performance trade-offs
  • Developing iterative optimisation strategies

Best Practices for Maintaining Edge AI Systems

  • Implementing versioning and continuous update protocols
  • Managing model rollback and compatibility
  • Addressing security and integrity considerations

Summary and Next Steps

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python-based model development
  • Working knowledge of neural network architectures

Target Audience

  • ML engineers
  • Data scientists
  • MLOps practitioners
 14 Hours

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