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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini’s capabilities within applications
- Integration paths for Firebase and SDKs
- Practical use cases for embedded AI
Preparing the Development Environment
- Configuring Firebase project settings
- Installing and setting up Vertex AI SDKs
- Hands-on lab: Establishing the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client applications
- Combining text, image, and audio functionalities
- Hands-on lab: Developing a Gemini-driven feature
Processing Multimodal Inputs
- Acquiring and handling user inputs (voice, image, text)
- Designing interactive workflows with Gemini
- Hands-on lab: Implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-enabled apps to production
- Tracking performance and usage via Firebase
- Hands-on lab: Deploying and testing applications
Security and Compliance Considerations
- Best practices for managing data in AI features
- Ensuring user privacy and consent within apps
- Hands-on lab: Securing an AI feature
Case Studies and Best Practices
- Examples of Gemini in consumer and enterprise applications
- Insights from real-world implementations
- Strategies for scalable AI features in applications
Conclusion and Future Steps
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- Proficiency in mobile or web application development
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
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