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Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining characteristics of edge-AI workloads
- Overview of Nano Banana’s architecture and core capabilities
- Strategic comparison between edge and cloud deployment models
Preparation for Edge-Ready Models
- Selecting appropriate models and establishing performance baselines
- Navigating dependency and compatibility requirements
- Exporting models to enable further optimization
Advanced Model Compression Methods
- Applying pruning strategies to achieve structural sparsity
- Utilizing weight sharing for parameter reduction
- Assessing the impact of compression on model quality
Quantization Strategies for Edge Efficiency
- Implementing post-training quantization techniques
- Integrating quantization-aware training pipelines
- Employing INT8, FP16, and mixed-precision formats
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana-specific acceleration features
- Seamlessly integrating ONNX standards with hardware backends
- Conducting rigorous benchmarks for accelerated inference
Executing Deployment on Edge Devices
- Embedding models into mobile and embedded applications
- Configuring runtime parameters and establishing monitoring
- Diagnosing and resolving common deployment challenges
Performance Analysis and Strategic Trade-offs
- Managing latency, throughput, and thermal constraints
- Optimizing the balance between accuracy and speed
- Applying iterative strategies for continuous optimization
Sustaining Robust Edge-AI Systems
- Implementing version control and continuous update protocols
- Managing model rollbacks and ensuring backward compatibility
- Addressing critical security and integrity standards
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of machine learning workflows
- Hands-on experience with Python-based model development
- Working knowledge of neural network architectures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
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