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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Understanding Gemini's capabilities within applications
- Integration pathways using Firebase and SDKs
- Practical use cases for embedded AI
Establishing the Development Environment
- Initiating and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Practical lab: Environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- 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-powered applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Strategies
- Best practices for data management in AI features
- Ensuring user privacy and consent within apps
- Practical lab: Securing AI functionalities
Case Studies and Industry Best Practices
- Examples of Gemini usage in consumer and enterprise applications
- Key takeaways from real-world implementations
- Strategies for scaling AI features effectively
Conclusion and Future Directions
Requirements
- Foundational programming skills in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Prior experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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