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Course Outline
Introduction to Vertex AI for Mobile and Web Applications
- Overview of Gemini capabilities within applications
- Pathways for Firebase and SDK integration
- Use cases for embedded AI
Setting Up the Development Environment
- Configuration and setup of Firebase projects
- Installation and configuration of Vertex AI SDKs
- Practical lab: environment setup
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio functionalities
- Practical lab: constructing a Gemini-powered feature
Multimodal Input Processing
- Capturing and processing user inputs (voice, image, text)
- Developing interactive application workflows with Gemini
- Practical lab: implementing multimodal input features
Application Deployment and Monitoring
- Deploying AI-enabled applications to production environments
- Tracking performance and usage via Firebase
- Practical lab: deploying and testing applications
Security and Compliance Considerations
- Data handling best practices for AI features
- Ensuring user privacy and obtaining consent within applications
- Practical lab: securing an AI feature
Case Studies and Best Practices
- Examples of Gemini integration in consumer and enterprise applications
- Insights gained from real-world implementations
- Best practices for scalable AI features in applications
Summary and Next Steps
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development
- Experience with Firebase or cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
Testimonials (1)
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