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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini capabilities within applications
- Integration pathways using Firebase and SDKs
- Practical use cases for embedded AI
Setting Up the Development Environment
- Configuring and setting up Firebase projects
- Installing and configuring Vertex AI SDKs
- Hands-on lab: Environment setup
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Integrating text, image, and audio functionalities
- Hands-on lab: Developing a Gemini-powered feature
Handling Multi-modal Inputs
- Capturing and processing user inputs (voice, images, text)
- Designing interactive app workflows with Gemini
- Hands-on lab: Implementing a multi-modal input feature
Application Deployment and Monitoring
- Deploying AI-enhanced applications to production
- Tracking performance and usage metrics via Firebase
- Hands-on lab: Deploying and testing applications
Security and Compliance Considerations
- Best practices for data handling in AI features
- Ensuring user privacy and obtaining consent within applications
- Hands-on lab: Securing an AI feature
Case Studies and Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Key takeaways from industry implementations
- Strategies for building scalable AI features in applications
Summary and Next Steps
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development
- Previous experience with Firebase or cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product management teams
14 Hours
Testimonials (1)
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