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Course Outline

Introduction to Vertex AI for Mobile & Web Apps

  • Overview of Gemini's capabilities within applications.
  • Integration pathways involving Firebase and SDKs.
  • Key use cases for embedded AI solutions.

Setting Up the Development Environment

  • Creation and configuration of Firebase projects.
  • Installation and setup of Vertex AI SDKs.
  • Hands-on lab: Environment configuration.

Embedding Gemini into Applications

  • Invoking Gemini APIs from client-side applications.
  • Integrating capabilities for text, image, and audio processing.
  • Hands-on lab: Developing a Gemini-powered feature.

Multimodal Input Handling

  • Capturing and processing user inputs (voice, image, text).
  • Designing interactive app workflows powered by Gemini.
  • Hands-on lab: Implementing multimodal input features.

App Deployment and Monitoring

  • Deploying AI-enabled applications to production.
  • Monitoring performance metrics and usage patterns 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 obtaining consent within apps.
  • Hands-on lab: Securing AI functionalities.

Case Studies and Best Practices

  • Examples of Gemini integration in consumer and enterprise applications.
  • Key insights derived from real-world implementations.
  • Best practices for building scalable AI features in apps.

Summary and Next Steps

Requirements

  • Fundamental programming proficiency in JavaScript, Kotlin, or Swift.
  • Familiarity with mobile or web application development processes.
  • Prior experience working with Firebase or cloud SDKs.

Target Audience

  • Mobile application developers.
  • Web application developers.
  • Product management and development teams.
 14 Hours

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