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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Examining Gemini's core capabilities within applications
- Exploring integration pathways via Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Setting up Vertex AI SDKs for development
- Practical lab: Initializing the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Implementing text, image, and audio interaction capabilities
- Practical lab: Developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, images, text)
- Designing interactive workflows with Gemini
- Practical lab: Implementing a multimodal input handler
Application Deployment and Monitoring
- Releasing AI-enhanced applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deployment and testing procedures
Security and Compliance Frameworks
- Adopting best practices for data management in AI features
- Ensuring user privacy and consent compliance in applications
- Practical lab: Securing AI feature implementations
Case Studies and Industry Standards
- Analyzing Gemini implementations in consumer and enterprise contexts
- Extracting insights from real-world deployment experiences
- Applying best practices for scalable in-app AI features
Conclusion and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development lifecycles
- Practical experience with Firebase or similar cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product development teams
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