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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 versus 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 the impact of compression.
Quantization for Edge Performance
- Post-training quantization methods.
- Quantization-aware training workflows.
- INT8, FP16, and mixed-precision approaches.
Acceleration with Nano Banana
- Utilizing 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 versus 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
- A foundational understanding of machine learning workflows.
- Experience with Python-based model development.
- Familiarity with neural network architectures.
Target 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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