Building On-Device AI Apps with Nano Banana Training Course
Nano Banana is a specialized model engineered to facilitate high-speed and efficient AI execution directly on end-user devices.
Designed as a live, instructor-led training session available both online and on-site, this course targets intermediate practitioners seeking to architect and release AI-driven mobile applications leveraging Nano Banana, entirely independent of cloud infrastructure.
Upon successfully completing this program, participants should be equipped to:
- Integrate Nano Banana models directly onto mobile platforms.
- Enhance AI workloads to achieve superior performance and energy efficiency.
- Embed text and image generation capabilities within mobile applications.
- Diagnose, benchmark, and optimize on-device inference pipelines.
Course Format
- Live, instructor-led demonstrations paired with interactive discussions.
- Practical exercises tailored to address real-world application scenarios.
- Direct hands-on development and testing within a live mobile environment.
Customization Options
- Should you require a personalized version of this curriculum, please reach out to discuss specific customization needs.
Course Outline
Getting Started with On-Device AI using Nano Banana
- Fundamental principles of on-device inference
- Exploring the Nano Banana model architecture and its capabilities
- Key deployment considerations for mobile operating systems
Setting Up Nano Banana and the Development Environment
- Installing the Nano Banana SDK and associated tools
- Configuring build environments for Android and iOS
- Handling dependencies and ensuring version compatibility
Executing Nano Banana Models on Mobile Hardware
- Loading and running pre-built models
- Navigating memory and computational limits on mobile devices
- Implementing strategies for real-time inference
Developing AI Features with Nano Banana
- Incorporating text generation features
- Building workflows for image generation and editing
- Processing multimodal inputs within applications
Optimizing Performance and Conducting Benchmarks
- Profiling latency and throughput
- Applying quantization, pruning, and model compression methods
- Optimizing for thermal management, battery life, and resource utilization
Security and Privacy in On-Device AI
- Managing local data handling and regulatory compliance
- Safeguarding models through secure execution environments
- Identifying risks and implementing mitigation strategies
Advanced Deployment Strategies
- Designing hybrid workflows combining on-device and cloud processing
- Managing offline-first AI application architectures
- Scaling solutions for large user bases
Testing, Debugging, and Continuous Refinement
- Implementing CI/CD pipelines for AI-enabled mobile apps
- Conducting unit, integration, and performance testing
- Managing iterative model updates and ensuring backward compatibility
Wrap-up and Future Directions
Requirements
- A solid grasp of mobile application development principles
- Proficiency in Python, Kotlin, or Swift
- Working knowledge of machine learning fundamentals
Target Audience
- Mobile application developers
- AI engineers
- Technical professionals investigating on-device AI deployment strategies
Open Training Courses require 5+ participants.
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Lukasz Kowalczyk - Allegro Sp. z o.o.
Course - Google Gemini AI for Data Analysis
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