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Course Outline

Overview of Edge AI and the Nano Banana Framework

  • Core attributes of edge-AI operational demands
  • Nano Banana architectural design and functional capabilities
  • Analysis of edge versus cloud deployment methodologies for government applications

Preparing AI Models for Edge Environment Integration

  • Model selection criteria and baseline performance assessment
  • Assessment of dependencies and system compatibility requirements
  • Model export procedures for subsequent optimization phases

Advanced Model Compression Methodologies

  • Pruning algorithms and structural sparsity implementation
  • Weight sharing mechanisms and parameter efficiency strategies
  • Assessment of performance impacts resulting from compression

Quantization Strategies for Enhanced Edge Performance

  • Implementation of post-training quantization protocols
  • Quantization-aware training procedures and workflows
  • Application of INT8, FP16, and mixed-precision computational standards

Leveraging Nano Banana for Computational Acceleration

  • Utilization of dedicated Nano Banana hardware accelerators
  • Integration of ONNX specifications with specialized hardware backends
  • Benchmarking inference rates to ensure service-level compliance

Executing Deployment to Edge Infrastructure

  • Incorporating models into embedded or mobile application stacks
  • Configuration of runtime parameters and continuous monitoring systems
  • Diagnostic and remediation procedures for deployment anomalies

Performance Profiling and Operational Trade-off Evaluation

  • Managing latency, throughput, and thermal management constraints
  • Balancing model accuracy against resource efficiency metrics
  • Strategies for iterative optimization and refinement

Best Practices for the Stewardship of Edge-AI Systems

  • Version control protocols and continuous update management
  • Model rollback procedures and compatibility assurance
  • Security hardening and data integrity safeguards for government platforms

Conclusions and Recommended Implementation Pathways

Requirements

  • Demonstrated proficiency in machine learning development workflows
  • Practical experience with Python-based model engineering
  • Technical familiarity with standard neural network architectures

Target Audience

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

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