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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities within apps
- Firebase and SDK integration strategies
- Key use cases for embedded AI solutions
Preparing the Development Environment
- Firebase project initialization and configuration
- Installation and setup of Vertex AI SDKs
- Practical lab: establishing the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Combining text, image, and audio features
- Practical lab: developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing user data (voice, image, text)
- Designing interactive workflows with Gemini
- Practical lab: implementing multimodal input capabilities
Application Deployment and Monitoring
- Releasing AI-enhanced apps to production
- Tracking performance and usage via Firebase
- Practical lab: deploying and validating applications
Security and Compliance Essentials
- Best practices for data management in AI features
- Ensuring user privacy and consent mechanisms
- Practical lab: securing AI components
Case Studies and Industry Best Practices
- Examples of Gemini in consumer and enterprise applications
- Insights from real-world deployment scenarios
- Strategies for building scalable in-app AI features
Recap and Future Directions
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- General familiarity with mobile or web application development
- Prior experience working with Firebase or cloud-based SDKs
Intended Audience
- Mobile developers
- Web developers
- Product management and engineering teams
14 Hours
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