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Course Outline
Introduction to Edge AI and Nano Banana for Government
- Key characteristics of edge-AI workloads for government applications
- Nano Banana architecture and capabilities aligned with public sector requirements
- Comparing edge versus cloud deployment strategies for government use cases
Preparing Models for Edge Deployment
- Model selection and baseline evaluation for government projects
- Dependency and compatibility considerations for government systems
- Exporting models for further optimization in government environments
Model Compression Techniques
- Pruning strategies and structural sparsity for efficient government operations
- Weight sharing and parameter reduction methods for government applications
- Evaluating the impacts of compression on government performance metrics
Quantization for Edge Performance
- Post-training quantization methods suitable for government use
- Quantization-aware training workflows for government deployments
- INT8, FP16, and mixed-precision approaches tailored for government needs
Acceleration with Nano Banana
- Utilizing Nano Banana accelerators in government contexts
- Integrating ONNX and hardware backends for government systems
- Benchmarking accelerated inference for government applications
Deployment to Edge Devices
- Integrating models into embedded or mobile applications for government use
- Runtime configuration and monitoring in government settings
- Troubleshooting deployment issues in government environments
Performance Profiling and Trade-off Analysis
- Latency, throughput, and thermal constraints for government operations
- Accuracy versus performance trade-offs for government applications
- Iterative optimization strategies for government systems
Best Practices for Maintaining Edge-AI Systems
- Versioning and continuous updates for government models
- Model rollback and compatibility management in government workflows
- Security and integrity considerations for government data
Summary and Next Steps
Requirements
- A comprehensive understanding of machine learning workflows for government applications
- Practical experience with Python-based model development for government projects
- Familiarity with neural network architectures and their implementation in public sector contexts
Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Practitioners
14 Hours
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