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Course Outline

Introduction to Vertex AI for Mobile & Web Apps

  • Overview of Gemini capabilities in applications
  • Firebase and SDK integration pathways
  • Use cases for embedded AI

Setting Up the Development Environment

  • Firebase project setup and configuration
  • 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

Multimodal Input Handling

  • Capturing and processing user input (voice, image, text)
  • Creating interactive application workflows with Gemini
  • Hands-on lab: implementing multimodal input features

Application Deployment and Monitoring

  • Deploying AI-powered applications to production
  • Monitoring performance and usage with Firebase
  • Hands-on lab: deploying and testing applications

Security and Compliance Considerations

  • Data handling best practices for AI features
  • User privacy and consent within applications
  • Hands-on lab: securing AI features

Case Studies and Best Practices

  • Examples of Gemini in consumer and enterprise applications
  • Lessons learned from real-world implementations
  • Best practices for scalable AI features in applications

Summary and Next Steps

Requirements

  • Basic programming knowledge in JavaScript, Kotlin, or Swift
  • Familiarity with mobile or web application development
  • Experience using Firebase or cloud SDKs

Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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