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Course Outline

Introduction to Edge AI and Nano Banana

  • Defining the core characteristics of edge-AI workloads.
  • Exploring Nano Banana’s architecture and key capabilities.
  • Contrasting edge-based versus cloud-based deployment strategies.

Preparing Models for Edge Deployment

  • Selecting appropriate models and conducting baseline evaluations.
  • Addressing dependency and compatibility requirements.
  • Exporting models to prepare them for further optimization.

Model Compression Techniques

  • Applying pruning strategies and structural sparsity.
  • Utilizing weight sharing and parameter reduction methods.
  • Assessing the impact of compression on model performance.

Quantization for Edge Performance

  • Executing post-training quantization methods.
  • Implementing quantization-aware training workflows.
  • Applying INT8, FP16, and mixed-precision techniques.

Acceleration with Nano Banana

  • Leveraging Nano Banana accelerators.
  • Integrating ONNX and hardware backends.
  • Benchmarking the performance of accelerated inference.

Deployment to Edge Devices

  • Embedding models into mobile or embedded applications.
  • Configuring and monitoring runtime environments.
  • Resolving common deployment challenges.

Performance Profiling and Trade-off Analysis

  • Analyzing latency, throughput, and thermal constraints.
  • Weighing the trade-offs between accuracy and performance.
  • Developing iterative optimization strategies.

Best Practices for Maintaining Edge-AI Systems

  • Managing versioning and continuous updates.
  • Handling model rollbacks and compatibility management.
  • Addressing security and integrity concerns.

Summary and Next Steps

Requirements

  • A solid grasp of machine learning workflows.
  • Proficiency in Python-based model development.
  • Familiarity with various neural network architectures.

Target Audience

  • ML Engineers.
  • Data Scientists.
  • MLOps Specialists.
 14 Hours

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