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Duration 14 hours
Course Outline
Overview of Edge AI and Nano Banana
- Defining features of edge-AI tasks
- Nano Banana’s design and functionalities
- Contrasting edge and cloud deployment approaches
Preparing Models for Edge Implementation
- Choosing models and establishing performance baselines
- Addressing dependencies and compatibility issues
- Exporting models to prepare for further refinement
Strategies for Model Compression
- Pruning methods and structural sparsity
- Parameter sharing and reduction techniques
- Assessing the effects of compression
Quantization for Enhanced Edge Performance
- Post-training quantization procedures
- Workflows for quantization-aware training
- Utilizing INT8, FP16, and mixed-precision formats
Optimizing with Nano Banana
- Utilizing Nano Banana acceleration capabilities
- Connecting ONNX standards with hardware backends
- Testing and measuring accelerated inference
Implementing on Edge Devices
- Embedding models into mobile or embedded systems
- Managing runtime settings and oversight
- Resolving common deployment challenges
Analyzing Performance and Trade-offs
- Managing latency, throughput, and heat limits
- Balancing precision against speed
- Adopting iterative improvement methods
Guidelines for Sustaining Edge-AI Systems
- Managing versions and ongoing updates
- Handling rollbacks and ensuring compatibility
- Focusing on security and data integrity
Conclusion and Future Actions
Requirements
- A solid grasp of machine learning processes
- Proficiency in Python-based model creation
- Knowledge of neural network structures
Target Audience
- ML engineers
- Data scientists
- MLOps professionals
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 2600 € + VAT*
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