Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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 for enhanced government operations
- Comparing edge versus cloud deployment strategies for government use cases
Preparing Models for Edge Deployment in Government Settings
- Model selection and baseline evaluation for government needs
- Dependency and compatibility considerations for government systems
- Exporting models for further optimization in government environments
Model Compression Techniques for Government Applications
- Pruning strategies and structural sparsity for efficient government operations
- Weight sharing and parameter reduction to enhance government performance
- Evaluating the impacts of compression on government models
Quantization for Edge Performance in Government Systems
- Post-training quantization methods for government use
- Quantization-aware training workflows to support government requirements
- INT8, FP16, and mixed-precision approaches for optimal government performance
Acceleration with Nano Banana for Government
- Utilizing Nano Banana accelerators in government applications
- Integrating ONNX and hardware backends for government systems
- Benchmarking accelerated inference for government operations
Deployment to Edge Devices for Government Use
- Integrating models into embedded or mobile applications for government purposes
- Runtime configuration and monitoring for government deployment
- Troubleshooting deployment issues in government settings
Performance Profiling and Trade-off Analysis for Government Systems
- Latency, throughput, and thermal constraints in government environments
- Accuracy versus performance trade-offs for government applications
- Iterative optimization strategies for government models
Best Practices for Maintaining Edge-AI Systems for Government
- Versioning and continuous updates for government systems
- Model rollback and compatibility management in government operations
- Security and integrity considerations for government edge-AI deployments
Summary and Next Steps for Government Implementation
Requirements
- An understanding of machine learning workflows for government applications
- Experience with Python-based model development
- Familiarity with neural network architectures
Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Practitioners
14 Hours
Testimonials (1)
Flow , vibe and topic on presentation
