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Course Outline

Introduction to Vertex AI for Mobile & Web Apps

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

Setting Up the Development Environment

  • Creating and configuring a Firebase project.
  • 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 functionalities.
  • Hands-on lab: Building a Gemini-powered feature.

Multimodal Input Handling

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

App Deployment and Monitoring

  • Deploying AI-enabled applications to production.
  • Monitoring performance and usage metrics with 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 scaling AI features in applications.

Summary and Next Steps

Requirements

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

Audience

  • Mobile developers.
  • Web developers.
  • Product teams.
 14 Hours

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