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Course Outline
Introduction to Vertex AI for Mobile and Web Applications
- Overview of Gemini’s capabilities within applications
- Integration pathways for Firebase and SDKs
- Key use cases for embedded AI
Configuring the Development Environment
- Firebase project initialization and setup
- Installation and configuration of Vertex AI SDKs
- Practical lab: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio functionalities
- Practical lab: Developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing user inputs (voice, image, text)
- Designing interactive app workflows using Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enabled apps to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Factors
- Best practices for data management in AI features
- User privacy considerations and consent management in apps
- Practical lab: Securing AI functionalities
Case Studies and Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Insights gained from actual implementations
- Best practices for building scalable AI features within apps
Conclusion and Recommended Next Steps
Requirements
- Foundational programming skills in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development
- Hands-on experience with Firebase or cloud-based SDKs
Target Audience
- Mobile application developers
- Web application developers
- Product development teams
14 Hours
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