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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini's capabilities within applications
- Integration pathways for Firebase and SDKs
- Key use cases for embedded AI
Establishing the Development Environment
- Setting up and configuring Firebase projects
- Installing and configuring Vertex AI SDKs
- Practical lab: Environment setup
Integrating Gemini into Applications
- Invoking Gemini APIs from client applications
- Combining text, image, and audio functionalities
- Practical lab: Developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing user input (voice, image, text)
- Designing interactive application workflows with Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Rolling out AI-driven applications to production
- Tracking performance and usage via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Considerations
- Best practices for data handling in AI features
- Ensuring user privacy and consent within applications
- Practical lab: Securing an AI feature
Case Studies and Industry Best Practices
- Examples of Gemini in consumer and enterprise applications
- Insights gained from real-world implementations
- Strategies for scalable AI features in applications
Summary and Future Directions
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift
- Proficiency in mobile or web application development
- Experience working with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
Testimonials (1)
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