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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini capabilities within applications.
- Integration pathways using 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 functionalities.
- Hands-on lab: building a Gemini-powered feature.
Multimodal Input Handling
- Capturing and processing user inputs (voice, image, text).
- Creating interactive app workflows using Gemini.
- Hands-on lab: implementing a multimodal input feature.
App Deployment and Monitoring
- Deploying AI-enabled applications to production.
- Monitoring performance and usage via Firebase.
- Hands-on lab: deploying and testing applications.
Security and Compliance Considerations
- Best practices for data handling in AI features.
- User privacy and consent management in applications.
- Hands-on lab: securing an AI feature.
Case Studies and Best Practices
- Examples of Gemini implementation in consumer and enterprise apps.
- Key lessons learned from real-world implementations.
- Best practices for developing scalable AI features in applications.
Summary and Next Steps
Requirements
- Foundational programming knowledge in JavaScript, Kotlin, or Swift.
- Familiarity with 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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