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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities within applications
- Integration pathways for Firebase and SDKs
- Key use cases for embedded AI
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Installation and setup of Vertex AI SDKs
- Practical lab: Environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio functionalities
- Practical lab: Developing a Gemini-powered feature
Managing Multimodal Inputs
- Capturing and processing user inputs (voice, images, text)
- Designing interactive workflows with Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enabled applications to production
- Tracking performance and usage via Firebase
- Practical lab: Deployment and testing workflows
Security and Compliance Implications
- Best practices for data handling in AI features
- User privacy and consent management in applications
- Practical lab: Securing AI-driven features
Case Studies and Industry Best Practices
- Examples of Gemini usage in consumer and enterprise apps
- Insights from real-world implementations
- Best practices for building scalable in-app AI features
Wrap-Up and Future Steps
Requirements
- Foundational programming skills 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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