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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining the core characteristics of edge-AI workloads
- Examining Nano Banana’s architecture and core capabilities
- Contrasting edge deployment with cloud-based strategies
Preparing Models for Edge Deployment
- Selecting models and establishing baseline evaluations
- Addressing dependency and compatibility factors
- Exporting models ready for further optimization
Model Compression Techniques
- Exploring pruning strategies and structural sparsity
- Applying weight sharing and parameter reduction
- Assessing the impact of compression methods
Quantization for Edge Performance
- Reviewing post-training quantization techniques
- Understanding quantization-aware training workflows
- Implementing INT8, FP16, and mixed-precision strategies
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX with hardware backends
- Conducting benchmarks for accelerated inference
Deployment to Edge Devices
- Embedding models into mobile or embedded applications
- Configuring and monitoring runtime environments
- Resolving common deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal limits
- Reconciling accuracy versus performance trade-offs
- Developing iterative optimization strategies
Best Practices for Maintaining Edge-AI Systems
- Managing version control and continuous updates
- Handling model rollbacks and compatibility
- Addressing security and integrity requirements
Summary and Next Steps
Requirements
- Working knowledge of machine learning workflows
- Hands-on experience with Python-based model development
- Familiarity with neural network architectures
Target Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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