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Duration 14 hours
Course Outline
Introduction to On-Device AI with Nano Banana
- Fundamental concepts of local inference processing
- Architectural details and capabilities of the Nano Banana model
- Key factors for deploying on mobile ecosystems
Environment Setup for Nano Banana
- Installation of Nano Banana SDK components
- Setting up build configurations for Android and iOS
- Handling library dependencies and version alignment
Executing Nano Banana Models on Mobile
- Loading and running pre-compiled models
- Navigating memory and computational limits on mobile chips
- Strategies for achieving real-time inference
Developing AI Features with Nano Banana
- Integrating text generation modules
- Creating workflows for image generation and editing
- Fusing multimodal data inputs in applications
Optimizing Performance and Benchmarking
- Profiling latency and throughput metrics
- Applying quantization, pruning, and compression methods
- Managing thermal output, battery drain, and resource consumption
Security and Privacy for On-Device AI
- Compliance and protocols for local data management
- Safeguarding models and ensuring secure runtime execution
- Identifying risks and implementing mitigation tactics
Advanced Deployment Architectures
- Designing hybrid workflows combining local and cloud processing
- Building offline-first AI application structures
- Scaling solutions for extensive user populations
Testing, Debugging, and Ongoing Refinement
- Implementing CI/CD pipelines for AI-capable mobile apps
- Conducting unit, integration, and performance validations
- Managing iterative model updates while ensuring backward compatibility
Recap and Future Pathways
Requirements
- A solid grasp of mobile application development lifecycles
- Practical proficiency in Python, Kotlin, or Swift
- Knowledge of fundamental machine learning principles
Target Audience
- Mobile application developers
- AI engineers
- Technical specialists investigating on-device AI deployment strategies
Testimonials (1)
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