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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini's capabilities within app ecosystems
- Integration strategies with Firebase and SDKs
- Practical use cases for embedded artificial intelligence
Preparing the Development Environment
- Initializing and configuring Firebase projects
- Installation and configuration of Vertex AI SDKs
- Practical exercise: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side code
- Blending text, image, and audio functionalities
- Practical exercise: Developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and interpreting user inputs (voice, image, text)
- Designing interactive workflows using Gemini
- Practical exercise: Implementing a multimodal input feature
Application Deployment and Oversight
- Releasing AI-enhanced applications to production
- Tracking performance and usage metrics via Firebase
- Practical exercise: Testing and deploying applications
Security and Compliance Frameworks
- Best practices for data management in AI features
- Ensuring user privacy and consent within apps
- Practical exercise: Securing AI-driven functionalities
Case Studies and Industry Standards
- Illustrations of Gemini in consumer and enterprise contexts
- Insights derived from real-world deployments
- Optimal practices for building scalable in-app AI features
Wrap-up and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Prior experience utilizing Firebase or cloud-based SDKs
Intended Audience
- Mobile application developers
- Web developers
- Product teams
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