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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini capabilities within app environments
- Integration pathways for Firebase and SDKs
- Key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Practical lab: Environment configuration
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 workflows using Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-powered applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deployment and testing procedures
Security and Compliance Factors
- Best practices for data management in AI features
- Ensuring user privacy and consent protocols in apps
- Practical lab: Securing AI-driven functionalities
Case Studies and Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Insights gained from practical implementations
- Strategies for building scalable AI features
Recap and Future Directions
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development principles
- Prior experience working with Firebase or cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product development teams
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