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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's potential within applications
- Integration pathways for Firebase and SDKs
- Practical use cases for embedded AI
Configuring the Development Environment
- Setting up and configuring Firebase projects
- Installation and configuration of Vertex AI SDKs
- Practical session: establishing the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client applications
- Incorporating text, image, and audio capabilities
- Practical session: developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, images, text)
- Designing interactive workflows with Gemini
- Practical session: implementing a multimodal input feature
Application Deployment and Oversight
- Releasing AI-enabled applications to production
- Tracking performance and usage metrics via Firebase
- Practical session: deploying and validating applications
Addressing Security and Compliance
- Best practices for data management in AI features
- Ensuring user privacy and obtaining consent in applications
- Practical session: implementing security measures for an AI feature
Case Studies and Industry Best Practices
- Examples of Gemini in consumer and enterprise contexts
- Key takeaways from real-world deployments
- Strategies for scalable AI features in applications
Recap and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development principles
- Prior exposure to Firebase or cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product management teams
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