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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the core characteristics of edge-AI workloads
- Examining Nano Banana’s architecture and functional capabilities
- Analyzing the distinctions between edge and cloud deployment strategies
Preparatory Steps for Edge Model Deployment
- Selecting appropriate models and establishing baseline performance metrics
- Addressing dependency and hardware compatibility issues
- Exporting models in preparation for further optimization
Advanced Model Compression Strategies
- Implementing pruning techniques and managing structural sparsity
- Utilizing weight sharing to reduce parameter counts
- Assessing the impact of compression on model quality
Quantization for Enhanced Edge Performance
- Applying post-training quantization methodologies
- Integrating quantization-aware training into the workflow
- Working with INT8, FP16, and mixed-precision formats
Performance Acceleration via Nano Banana
- Leveraging Nano Banana’s acceleration features
- Connecting with ONNX standards and various hardware backends
- Conducting benchmarks for accelerated inference
Deployment to Edge Infrastructure
- Embedding models into mobile or embedded applications
- Configuring runtimes and establishing monitoring protocols
- Resolving common deployment challenges
Performance Analysis and Strategic Trade-offs
- Managing latency, throughput, and thermal limitations
- Balancing accuracy against performance requirements
- Employing iterative optimization methods
Sustaining Edge-AI System Integrity
- Managing version control and continuous updates
- Handling model rollbacks and ensuring compatibility
- Addressing security and data integrity concerns
Conclusions and Future Directions
Requirements
- A solid grasp of machine learning pipelines
- Practical experience in Python-based model development
- Knowledge of standard neural network architectures
Intended Participants
- ML Engineers
- Data Scientists
- MLOps Practitioners
Testimonials (1)
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