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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
- Use cases for embedded AI solutions
Configuring the Development Environment
- Setting up and configuring Firebase projects
- Installing and configuring 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: constructing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and processing user inputs, including voice, images, and text
- Designing interactive application workflows with Gemini
- Practical lab: implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-powered applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: deploying and testing applications
Security and Compliance Considerations
- Best practices for data management in AI features
- Ensuring user privacy and consent within applications
- Practical lab: implementing security measures for an AI feature
Case Studies and Best Practices
- Illustrations of Gemini usage in consumer and enterprise applications
- Insights derived from real-world implementations
- Best practices for achieving scalable AI features in applications
Recap and Recommended Next Steps
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development principles
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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