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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini's capabilities within applications
- Integration pathways for Firebase and SDKs
- Application scenarios for embedded AI
Configuring the Development Environment
- Firebase project initialization and configuration
- Installation and setup of Vertex AI SDKs
- Practical lab: Environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio capabilities
- Practical lab: Developing a Gemini-driven feature
Processing Multimodal Inputs
- Acquiring and processing user inputs (voice, image, text)
- Designing interactive app workflows utilizing Gemini
- Practical lab: Multimodal input functionality
Application Deployment and Monitoring
- Releasing AI-enabled apps to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deployment and application testing
Security and Compliance Frameworks
- Best practices for data management in AI features
- User privacy and consent protocols within apps
- Practical lab: Securing an AI feature
Case Studies and Industry Best Practices
- Case examples of Gemini in consumer and enterprise applications
- Insights from real-world deployments
- Strategies for scalable AI features in apps
Conclusions and Subsequent Steps
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- Proficiency in mobile or web application development
- Prior experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
Testimonials (1)
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