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Duration 14 hours
Course Outline
Introduction to Edge AI and the Nano Banana Ecosystem
- Defining the core attributes of edge-AI workloads.
- Overview of Nano Banana’s architecture and core capabilities.
- Analytical comparison of edge versus cloud deployment strategies.
Model Preparation for Edge Environments
- Model selection criteria and establishing baseline performance.
- Assessing dependencies and system compatibility.
- Exporting models to enable further optimization.
Strategies for Model Compression
- Pruning methodologies and achieving structural sparsity.
- Parameter reduction through weight sharing techniques.
- Assessing the impact of compression on model performance.
Quantization for Optimized Edge Performance
- Techniques for post-training quantization.
- Workflows for quantization-aware training.
- Application of INT8, FP16, and mixed-precision formats.
Accelerating Inference with Nano Banana
- Leveraging Nano Banana’s hardware accelerators.
- Integration with ONNX and specific hardware backends.
- Conducting benchmarks for accelerated inference performance.
Deploying Models to Edge Hardware
- Embedding models into mobile or embedded applications.
- Configuring runtime parameters and establishing monitoring systems.
- Diagnosing and resolving common deployment challenges.
Performance Profiling and Strategic Trade-offs
- Managing constraints related to latency, throughput, and thermal limits.
- Navigating the balance between model accuracy and operational performance.
- Implementing iterative optimization workflows.
Best Practices for Sustaining Edge-AI Systems
- Managing version control and facilitating continuous updates.
- Handling model rollbacks and ensuring long-term compatibility.
- Addressing security protocols and data integrity requirements.
Wrap-up and Future Directions
Requirements
- Foundational knowledge of machine learning pipelines.
- Proficiency in Python-based model development.
- Understanding of neural network structures.
Target Audience
- Machine Learning Engineers.
- Data Scientists.
- MLOps Specialists.
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