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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining the primary characteristics of edge-AI workloads
- Exploring Nano Banana’s architectural design and capabilities
- Analyzing the differences between edge and cloud deployment strategies
Readying Models for Edge Implementation
- Selecting appropriate models and establishing baseline evaluations
- Addressing dependency and compatibility requirements
- Exporting models to prepare for advanced optimization
Advanced Model Compression Methods
- Applying pruning strategies and introducing structural sparsity
- Utilizing weight sharing and reducing parameter counts
- Measuring the impact of compression on model integrity
Quantization Strategies for Edge Efficiency
- Implementing post-training quantization techniques
- Managing quantization-aware training workflows
- Working with INT8, FP16, and mixed-precision formats
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana’s acceleration features
- Integrating ONNX models with various hardware backends
- Performing benchmarks on accelerated inference tasks
Deploying Models to Edge Hardware
- Embedding models within mobile or embedded applications
- Configuring and monitoring runtime behavior
- Diagnosing and resolving common deployment challenges
Performance Analysis and Trade-off Evaluation
- Managing latency, throughput, and thermal limitations
- Balancing model accuracy against performance metrics
- Developing iterative optimization approaches
Best Practices for Sustaining Edge-AI Systems
- Managing model versioning and continuous updates
- Handling rollbacks and ensuring compatibility management
- Addressing security and data integrity requirements
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with model development in Python
- Knowledge of neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Practitioners
Testimonials (1)
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