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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- An overview of Gemini's capabilities within application contexts
- Exploring integration pathways for Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Hands-on lab: Building the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Seamlessly integrating text, image, and audio processing capabilities
- Hands-on lab: Developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and processing diverse user inputs, including voice, images, and text
- Designing interactive app workflows leveraging Gemini
- Hands-on lab: Implementing multimodal input features
App Deployment and Monitoring
- Releasing AI-powered applications to production
- Tracking performance and usage metrics using Firebase
- Hands-on lab: Deploying and testing application builds
Addressing Security and Compliance
- Applying best practices for data management in AI features
- Ensuring user privacy and consent compliance within apps
- Hands-on lab: Enhancing the security posture of an AI feature
Case Studies and Best Practices
- Examining examples of Gemini in consumer and enterprise applications
- Deriving insights from real-world implementation experiences
- Adopting best practices for building scalable AI features
Conclusion and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- General familiarity with mobile or web application development principles
- Practical experience utilizing Firebase or other cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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