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 Duration 14 hours

Course Outline

Overview of Edge AI and Nano Banana

  • Defining core attributes of edge AI workloads
  • Detailed analysis of Nano Banana system architecture and functional capabilities
  • Strategic comparison of edge-based versus cloud-based deployment models

Preparation of Models for Edge Integration

  • Criteria for model selection and initial performance benchmarking
  • Assessment of library dependencies and system compatibility
  • Procedures for exporting models to enable downstream optimization

Techniques for Model Compression

  • Application of pruning methods and structural sparsity reduction
  • Implementation of weight sharing and parameter minimization strategies
  • Metric-based evaluation of compression effects on model fidelity

Quantization Strategies for Edge Performance

  • Methodologies for post-training quantization implementation
  • Workflows for quantization-aware training integration
  • Technical standards for INT8, FP16, and mixed-precision formats

Acceleration Utilizing Nano Banana

  • Leveraging Nano Banana specific acceleration modules
  • Integration of ONNX standards with proprietary hardware backends
  • Performance benchmarking of accelerated inference pipelines

Deployment to Edge Infrastructure

  • Embedding optimized models into mobile and embedded operational systems
  • Configuration of runtime parameters and continuous monitoring protocols
  • Diagnostic procedures for resolving deployment anomalies

Performance Profiling and Strategic Trade-off Analysis

  • Analysis of latency, throughput, and thermal management constraints
  • Quantitative assessment of accuracy versus performance balance
  • Frameworks for iterative system optimization and refinement

Best Practices for Maintaining Edge AI Systems

  • Protocols for version control and continuous system updates
  • Strategies for model rollback and cross-version compatibility
  • Standards for data security and system integrity preservation

Summary and Recommended Next Steps

Requirements

  • Demonstrated proficiency in machine learning operational workflows
  • Practical experience with Python-based model development and engineering
  • Technical familiarity with various neural network architectural designs

Target Audience

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

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