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Course Outline

Introduction to Edge AI and the Nano Banana Framework

  • Defining the key attributes of edge-AI workloads.
  • Overview of Nano Banana’s architecture and core capabilities.
  • An analysis of edge versus cloud deployment strategies.

Preparing Models for Edge Deployment

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

Techniques for Model Compression

  • Exploring pruning strategies and structural sparsity.
  • Implementing weight sharing and reducing parameters.
  • Assessing the impact of compression on model performance.

Quantization for Enhanced Edge Performance

  • Methods for post-training quantization.
  • Workflows for quantization-aware training.
  • Utilizing INT8, FP16, and mixed-precision techniques.

Acceleration Leveraging Nano Banana

  • Leveraging Nano Banana’s built-in accelerators.
  • Integrating ONNX standards with hardware backends.
  • Benchmarking performance for accelerated inference.

Deploying to Edge Devices

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

Performance Profiling and Trade-off Analysis

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

Best Practices for Edge-AI System Maintenance

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

Conclusion and Recommended Next Steps

Requirements

  • A foundational understanding of machine learning workflows.
  • Hands-on experience with Python-based model development.
  • Familiarity with various neural network architectures.

Target Audience

  • ML Engineers
  • Data Scientists
  • MLOps Practitioners
 14 Hours

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