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Course Outline

Introduction to Edge AI and Nano Banana

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

Preparing Models for Edge Deployment

  • Selecting appropriate models and establishing baseline evaluations.
  • Addressing dependency and compatibility requirements.
  • Exporting models for subsequent optimization stages.

Model Compression Techniques

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

Quantization for Edge Performance

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

Acceleration with Nano Banana

  • Leveraging Nano Banana accelerators for performance gains.
  • Integrating ONNX formats and hardware-specific backends.
  • Conducting benchmarks for accelerated inference.

Deployment to Edge Devices

  • Embedding models into mobile or embedded application ecosystems.
  • Configuring runtime settings and implementing monitoring solutions.
  • Diagnosing and resolving common deployment issues.

Performance Profiling and Trade-off Analysis

  • Managing latency, throughput, and thermal limitations.
  • Navigating the balance between model accuracy and operational performance.
  • Developing iterative optimization strategies for continuous improvement.

Best Practices for Maintaining Edge-AI Systems

  • Implementing version control and continuous update mechanisms.
  • Handling model rollbacks and ensuring compatibility management.
  • Addressing security and data integrity considerations.

Summary and Next Steps

Requirements

  • A solid grasp of machine learning workflows and pipelines.
  • Practical experience in model development using Python.
  • Foundational familiarity with various neural network architectures.

Intended Audience

  • ML engineers.
  • Data scientists.
  • MLOps practitioners.
 14 Hours

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