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Course Outline

Foundations of Edge AI and the Nano Banana Framework

  • Defining the core attributes of edge-AI workloads
  • Exploring Nano Banana’s architecture and functional capabilities
  • Evaluating edge versus cloud deployment strategies

Readying Models for Edge Environments

  • Selecting appropriate models and establishing baseline metrics
  • Assessing dependency and compatibility requirements
  • Exporting models for subsequent optimization phases

Advanced Model Compression Methods

  • Applying pruning strategies and achieving structural sparsity
  • Leveraging weight sharing and parameter minimization
  • Measuring the impact of compression on model quality

Optimizing Edge Performance through Quantization

  • Implementing post-training quantization techniques
  • Managing quantization-aware training pipelines
  • Utilizing INT8, FP16, and mixed-precision strategies

Performance Acceleration via Nano Banana

  • Deploying Nano Banana hardware accelerators
  • Connecting ONNX standards with specific hardware backends
  • Conducting benchmarks for accelerated inference tasks

Implementing Deployment on Edge Devices

  • Embedding models into mobile or embedded applications
  • Managing runtime configurations and performance monitoring
  • Resolving common deployment challenges

Performance Analysis and Trade-off Management

  • Addressing latency, throughput, and thermal limitations
  • Balancing accuracy against operational performance
  • Applying iterative optimization approaches

Best Practices for Sustaining Edge-AI Systems

  • Managing version control and continuous updates
  • Handling model rollbacks and compatibility standards
  • Safeguarding system security and data integrity

Wrap-up and Future Recommendations

Requirements

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

Target Audience

  • ML Engineers
  • Data Scientists
  • MLOps Practitioners
 14 Hours

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