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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the core attributes of edge-AI workloads
- Overview of Nano Banana’s architecture and functional capabilities
- Contrasting edge deployment with cloud-based strategies
Readying Models for Edge Integration
- Choosing appropriate models and establishing baseline metrics
- Addressing dependency and compatibility requirements
- Preparing models for export in preparation for optimization
Strategies for Model Compression
- Exploring pruning methodologies and structural sparsity
- Applying weight sharing and reducing parameter count
- Assessing the impact of compression on model performance
Optimizing Performance via Quantization
- Techniques for post-training quantization
- Implementing quantization-aware training processes
- Utilizing INT8, FP16, and mixed-precision strategies
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana accelerator features
- Incorporating ONNX standards and hardware backends
- Conducting benchmarks for accelerated inference
Executing Deployment on Edge Devices
- Embedding models into mobile or embedded applications
- Managing runtime settings and monitoring activities
- Resolving common deployment challenges
Conducting Performance Profiling and Trade-off Assessments
- Managing latency, throughput, and thermal limitations
- Navigating the trade-offs between accuracy and performance
- Applying iterative optimization tactics
Best Practices for Sustaining Edge-AI Systems
- Managing version control and continuous system updates
- Handling model rollbacks and compatibility issues
- Addressing security and integrity protocols
Review and Future Directions
Requirements
- A solid grasp of machine learning pipelines
- Proficiency in Python-based model development
- Knowledge of neural network structures
Target Audience
- ML engineers
- Data scientists
- MLOps specialists
Testimonials (1)
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