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Course Outline
Introduction to Edge AI and Nano Banana
- Key characteristics of edge-AI workloads
- Nano Banana architecture and capabilities
- Comparing edge vs cloud deployment strategies
Preparing Models for Edge Deployment
- Model selection and baseline evaluation
- Dependency and compatibility considerations
- Exporting models for further optimization
Model Compression Techniques
- Pruning strategies and structural sparsity
- Weight sharing and parameter reduction
- Evaluating compression impacts
Quantization for Edge Performance
- Post-training quantization methods
- Quantization-aware training workflows
- INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Using Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Runtime configuration and monitoring
- Troubleshooting deployment issues
Performance Profiling and Trade-off Analysis
- Latency, throughput, and thermal constraints
- Accuracy vs performance trade-offs
- Iterative optimization strategies
Best Practices for Maintaining Edge-AI Systems
- Versioning and continuous updates
- Model rollback and compatibility management
- Security and integrity considerations
Summary and Next Steps
Requirements
- An understanding of machine learning workflows
- Experience with Python-based model development
- Familiarity with neural network architectures
Audience
- ML engineers
- Data scientists
- MLOps practitioners
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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