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Duration 14 hours
Course Outline
Overview of Edge AI and the Nano Banana Framework
- Defining the core attributes of edge-AI workloads
- Examining Nano Banana’s architecture and functional capabilities
- Contrasting edge-based versus cloud-based deployment strategies
Readying Models for Edge Environments
- Selecting models and establishing performance baselines
- Addressing dependency and compatibility requirements
- Exporting models to enable further optimization
Strategies for Model Compression
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing and parameter reduction
- Assessing the impact of compression techniques
Quantization for Optimal Edge Performance
- Techniques for post-training quantization
- Workflows for quantization-aware training
- Implementing INT8, FP16, and mixed-precision methods
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana’s acceleration features
- Integrating ONNX standards and hardware backends
- Conducting benchmarks for accelerated inference
Deploying to Edge Devices
- Embedding models into mobile or embedded applications
- Configuring runtimes and implementing monitoring
- Diagnosing and resolving deployment challenges
Performance Analysis and Trade-off Evaluation
- Managing latency, throughput, and thermal limits
- Reconciling accuracy with performance goals
- Adopting iterative optimization approaches
Maintaining Edge-AI Systems: Best Practices
- Implementing versioning and continuous updates
- Managing model rollbacks and compatibility
- Addressing security and data integrity
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience developing models with Python
- Working knowledge of neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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