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Course Outline

Fundamentals of Edge AI and an Introduction to Nano Banana

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

Readying Models for Edge Integration

  • Choosing models and establishing performance baselines.
  • Assessing dependencies and hardware compatibility.
  • Preparing model exports for further refinement.

Techniques for Model Compression

  • Applying pruning methods and achieving structural sparsity.
  • Utilizing weight sharing to reduce parameter count.
  • Measuring the effect of compression on model quality.

Quantization Strategies for Edge Efficiency

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

Accelerating Performance with Nano Banana

  • Leveraging Nano Banana’s acceleration features.
  • Integrating with ONNX and specific hardware backends.
  • Conducting benchmarks on accelerated inference tasks.

Deploying to Edge Hardware

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

Analyzing Performance and Trade-offs

  • Managing latency, throughput, and thermal limits.
  • Balancing accuracy against performance metrics.
  • Employing iterative approaches to optimization.

Maintaining Robust Edge-AI Systems

  • Managing version control and continuous updates.
  • Handling model rollbacks and compatibility issues.
  • Addressing security and system integrity requirements.

Wrap-up and Recommended Next Steps

Requirements

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

Target Audience

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

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