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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini capabilities in applications
- Integration pathways for Firebase and SDKs
- 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
Integrating Gemini into Applications
- Calling Gemini APIs from client apps
- Integrating text, image, and audio capabilities
- Hands-on lab: developing a Gemini-powered feature
Handling Multimodal Inputs
- Capturing and processing user inputs (voice, image, text)
- Developing interactive app workflows with Gemini
- Hands-on lab: implementing multimodal input features
Application Deployment and Monitoring
- Deploying AI-powered apps 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 within applications
- Hands-on lab: securing AI features
Case Studies and Best Practices
- Examples of Gemini integration 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
- Prior experience with Firebase or cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
Testimonials (1)
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