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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

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

Configuring the Development Environment

  • Setting up and configuring Firebase projects
  • Installing and configuring Vertex AI SDKs
  • Practical lab: Environment initialization

Integrating Gemini into Applications

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

Managing Multimodal Input

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

App Deployment and Monitoring

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

Security and Compliance Factors

  • Best practices for data management in AI features
  • User privacy and consent mechanisms in applications
  • Practical lab: Securing AI-enabled components

Case Studies and Best Practices

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

Conclusion and Next Steps

Requirements

  • Foundational programming proficiency in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development workflows
  • Prior experience with Firebase or cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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