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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities within applications
- Integration pathways for Firebase and SDKs
- Use cases for embedded AI solutions
Establishing the Development Environment
- Firebase project initialization and configuration
- Installation and setup of Vertex AI SDKs
- Hands-on lab: environment preparation
Integrating Gemini into Applications
- Invoking Gemini APIs from client applications
- Incorporating text, image, and audio processing capabilities
- Hands-on lab: developing a Gemini-powered feature
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, images, text)
- Designing interactive application workflows with Gemini
- Hands-on lab: implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enabled apps to production
- Tracking performance and usage metrics via Firebase
- Hands-on lab: deploying and testing applications
Addressing Security and Compliance
- Best practices for data management in AI features
- User privacy considerations and consent mechanisms in apps
- Hands-on lab: securing AI functionalities
Case Studies and Industry Best Practices
- Illustrations of Gemini in consumer and enterprise applications
- Insights from real-world implementation experiences
- Strategies for scalable AI features in applications
Wrap-Up and Future Steps
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
Testimonials (1)
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