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Duration 14 hours
Course Outline
Introduction to Edge AI and Nano Banana
- Defining key characteristics of edge-AI workloads
- Overview of Nano Banana architecture and core capabilities
- Contrasting edge versus cloud deployment strategies
Preparation of Models for Edge Deployment
- Model selection and establishing baseline evaluations
- Assessing dependencies and compatibility requirements
- Exporting models to prepare for further optimisation
Techniques for Model Compression
- Applying pruning strategies and structural sparsity
- Utilising weight sharing and parameter reduction
- Assessing the impact of compression on performance
Quantisation Strategies for Edge Performance
- Implementing post-training quantisation methods
- Integrating quantisation-aware training workflows
- Exploring INT8, FP16, and mixed-precision approaches
Accelerating Inference with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference results
Deploying to Edge Devices
- Integrating models into embedded or mobile applications
- Configuring runtimes and establishing monitoring systems
- Resolving common deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal constraints
- Analyising accuracy versus performance trade-offs
- Applying iterative optimisation strategies
Best Practices for Maintaining Edge-AI Systems
- Implementing versioning and continuous update protocols
- Managing model rollbacks and compatibility
- Addressing security and data integrity considerations
Summary and Future Directions
Requirements
- A solid understanding of machine learning workflows
- Proficiency in Python-based model development
- Familiarity with neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
Testimonials (1)
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