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Course Outline

Introduction to Vertex AI for Mobile and Web Applications

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

Configuring the Development Environment

  • Firebase project initialization and setup
  • Installation and configuration of Vertex AI SDKs
  • Practical lab: Setting up the development environment

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Incorporating text, image, and audio functionalities
  • Practical lab: Developing a Gemini-driven feature

Managing Multimodal Inputs

  • Capturing and processing user inputs (voice, image, text)
  • Designing interactive app workflows using Gemini
  • Practical lab: Implementing multimodal input features

Application Deployment and Monitoring

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

Security and Compliance Factors

  • Best practices for data management in AI features
  • User privacy considerations and consent management in apps
  • Practical lab: Securing AI functionalities

Case Studies and Best Practices

  • Real-world examples of Gemini in consumer and enterprise apps
  • Insights gained from actual implementations
  • Best practices for building scalable AI features within apps

Conclusion and Recommended Next Steps

Requirements

  • Foundational programming skills in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development
  • Hands-on experience with Firebase or cloud-based SDKs

Target Audience

  • Mobile application developers
  • Web application developers
  • Product development teams
 14 Hours

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