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Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini capabilities within applications.
- Firebase and SDK integration pathways.
- Use cases for embedded AI.
Setting Up the Development Environment
- Firebase project setup and configuration.
- Installing and configuring 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 input (voice, image, text).
- Creating interactive application workflows with Gemini.
- Hands-on lab: Multimodal input feature implementation.
Application 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
- Data handling best practices for AI features.
- User privacy and consent within applications.
- Hands-on lab: Securing an AI feature.
Case Studies and Best Practices
- Examples of Gemini in consumer and enterprise applications.
- Lessons learned 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.
- Familiarity with mobile or web application development.
- Experience utilizing Firebase or cloud SDKs.
Target Audience
- Mobile developers.
- Web developers.
- Product teams.
14 Hours
Testimonials (1)
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