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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities within application contexts
- Integration paths using Firebase and SDKs
- Key use cases for embedded artificial intelligence
Configuring the Development Environment
- Initiating and configuring Firebase projects
- Installation and configuration of Vertex AI SDKs
- Practical lab: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: Developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and processing diverse user inputs such as voice, images, and text
- Designing interactive application workflows utilizing Gemini
- Practical lab: Implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-driven applications to production environments
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and testing application builds
Security and Compliance Protocols
- Best practices for handling data in AI-enabled features
- Ensuring user privacy and managing consent within applications
- Practical lab: Securing an AI-driven feature
Case Studies and Best Practices
- Real-world examples of Gemini in consumer and enterprise applications
- Insights gained from practical implementation scenarios
- Strategies for building scalable AI features within apps
Conclusion and Recommendations for Next Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development workflows
- Prior experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product management and development teams
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