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Course Outline

Foundations of Edge AI and Nano Banana

  • Defining the core attributes of edge AI workloads
  • Understanding the architecture and core capabilities of Nano Banana
  • Analyzing the differences between edge and cloud deployment strategies

Readying Models for Edge Environments

  • Selecting appropriate models and establishing baseline evaluations
  • Addressing dependencies and compatibility requirements
  • Exporting models to prepare for advanced optimization

Advanced Model Compression Methods

  • Applying pruning strategies and structural sparsity
  • Utilizing weight sharing to reduce parameters
  • Measuring the impact of compression on model quality

Quantization for Enhanced Edge Performance

  • Employing post-training quantization methods
  • Implementing quantization-aware training workflows
  • Exploring INT8, FP16, and mixed-precision techniques

Leveraging Nano Banana for Acceleration

  • Utilizing Nano Banana’s acceleration features
  • Integrating ONNX formats and specific hardware backends
  • Conducting benchmarks for accelerated inference

Deploying to Edge Hardware

  • Embedding models into mobile or embedded applications
  • Configuring runtimes and establishing monitoring systems
  • Resolving common deployment challenges

Analyzing Performance and Trade-offs

  • Managing latency, throughput, and thermal limits
  • Balancing accuracy against performance demands
  • Applying iterative optimization strategies

Best Practices for Sustaining Edge AI Systems

  • Implementing versioning and continuous update protocols
  • Managing model rollbacks and ensuring compatibility
  • Addressing security and system integrity concerns

Wrap-Up and Future Directions

Requirements

  • A solid grasp of machine learning workflows
  • Practical experience in developing models with Python
  • Working knowledge of neural network architectures

Target Audience

  • ML engineers
  • Data scientists
  • MLOps practitioners
 14 Hours

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