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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Examining Gemini capabilities within applications
- Exploring Firebase and SDK integration routes
- Identifying key use cases for embedded AI
Establishing the Development Environment
- Initializing and configuring Firebase projects
- Setting up and adjusting Vertex AI SDKs
- Practical lab: Preparing the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: Developing a Gemini-driven feature
Processing Multimodal Inputs
- Capturing and interpreting user inputs (voice, image, text)
- Designing interactive workflows using Gemini
- Practical lab: Implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-enhanced apps to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and validating applications
Security and Compliance Frameworks
- Adopting best practices for data management in AI features
- Ensuring user privacy and consent mechanisms within apps
- Practical lab: Securing an AI-integrated feature
Case Studies and Industry Best Practices
- Reviewing Gemini implementations in consumer and enterprise sectors
- Analyzing insights from real-world deployments
- Adopting best practices for scalable in-app AI features
Recap and Future Directions
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product teams
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