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Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini’s capabilities within applications
  • Integration paths for Firebase and SDKs
  • Practical use cases for embedded AI

Preparing the Development Environment

  • Configuring Firebase project settings
  • Installing and setting up Vertex AI SDKs
  • Hands-on lab: Establishing the development environment

Integrating Gemini into Applications

  • Invoking Gemini APIs from client applications
  • Combining text, image, and audio functionalities
  • Hands-on lab: Developing a Gemini-driven feature

Processing Multimodal Inputs

  • Acquiring and handling user inputs (voice, image, text)
  • Designing interactive workflows with Gemini
  • Hands-on lab: Implementing a multimodal input feature

Application Deployment and Monitoring

  • Releasing AI-enabled apps to production
  • Tracking performance and usage via Firebase
  • Hands-on lab: Deploying and testing applications

Security and Compliance Considerations

  • Best practices for managing data in AI features
  • Ensuring user privacy and consent within apps
  • Hands-on lab: Securing an AI feature

Case Studies and Best Practices

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

Conclusion and Future Steps

Requirements

  • Fundamental programming skills in JavaScript, Kotlin, or Swift
  • Proficiency in mobile or web application development
  • Practical experience with Firebase or cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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