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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini capabilities within applications
- Firebase and SDK integration strategies
- Key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Practical exercise: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio functionalities
- Practical exercise: Developing a Gemini-powered feature
Managing Multimodal Input
- Capturing and processing user inputs (voice, image, text)
- Designing interactive workflows with Gemini
- Practical exercise: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-driven applications to production
- Tracking performance and usage metrics via Firebase
- Practical exercise: Deploying and testing applications
Addressing Security and Compliance
- Best practices for data management in AI features
- Ensuring user privacy and consent mechanisms in apps
- Practical exercise: Securing an AI feature
Case Studies and Best Practices
- Illustrations of Gemini in consumer and enterprise contexts
- Insights from real-world implementation challenges
- Strategies for scaling AI features effectively
Conclusion and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development principles
- Prior experience utilizing Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
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