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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini capabilities within applications.
- Integration pathways using Firebase and SDKs.
- Practical use cases for embedded AI.
Setting Up the Development Environment
- Setting up and configuring Firebase projects.
- Installing and configuring Vertex AI SDKs.
- Hands-on lab: Environment setup.
Embedding Gemini into Applications
- Invoking Gemini APIs from client applications.
- Integrating text, image, and audio capabilities.
- Hands-on lab: Building a Gemini-powered feature.
Handling Multimodal Inputs
- Capturing and processing user inputs (voice, image, text).
- Designing interactive app workflows powered by Gemini.
- Hands-on lab: Implementing multimodal input features.
Application Deployment and Monitoring
- Deploying AI-enabled applications to production.
- Monitoring performance and usage metrics via Firebase.
- Hands-on lab: Deploying and testing applications.
Security and Compliance Considerations
- Best practices for data handling in AI features.
- Ensuring user privacy and consent within applications.
- Hands-on lab: Securing an AI feature.
Case Studies and Best Practices
- Examples of Gemini integration in consumer and enterprise applications.
- Key lessons learned from real-world implementations.
- Best practices for building scalable AI features in apps.
Summary and Next Steps
Requirements
- Foundational programming knowledge in JavaScript, Kotlin, or Swift.
- Familiarity with mobile or web application development.
- Practical experience using Firebase or cloud SDKs.
Audience
- Mobile developers.
- Web developers.
- Product teams.
14 Hours
Testimonials (1)
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