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Course Outline

Introduction to Vertex AI for Mobile and Web Applications

  • Overview of Gemini capabilities within apps
  • Integration pathways for Firebase and SDKs
  • Use cases for embedded AI

Setting Up the Development Environment

  • Firebase project setup and configuration
  • Installation and configuration of 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 app workflows with Gemini
  • Hands-on lab: Implementing a multimodal input feature

App Deployment and Monitoring

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

Security and Compliance Considerations

  • Data handling best practices for AI features
  • User privacy and consent mechanisms in applications
  • Hands-on lab: Securing an AI feature

Case Studies and Best Practices

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

Summary and Next Steps

Requirements

  • Fundamental programming knowledge in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development
  • Experience working with Firebase or cloud SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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