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Course Outline
Introduction to Vertex AI for Mobile and Web Applications
- Overview of Gemini capabilities within apps
- Integration pathways for Firebase and SDKs
- Use cases for embedded AI
Setting Up the Development Environment
- Firebase project setup and configuration
- Installation and configuration of Vertex AI SDKs
- Hands-on lab: Environment setup
Embedding Gemini into Applications
- Invoking Gemini APIs from client applications
- Integrating text, image, and audio capabilities
- Hands-on lab: Building a Gemini-powered feature
Multimodal Input Handling
- Capturing and processing user input (voice, image, text)
- Creating interactive app workflows with Gemini
- Hands-on lab: Implementing a multimodal input feature
App Deployment and Monitoring
- Deploying AI-powered applications to production
- Monitoring performance and usage via Firebase
- Hands-on lab: Deploying and testing applications
Security and Compliance Considerations
- Data handling best practices for AI features
- User privacy and consent mechanisms in applications
- Hands-on lab: Securing an AI feature
Case Studies and Best Practices
- Examples of Gemini usage in consumer and enterprise applications
- Insights 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 working with Firebase or cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
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