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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Examining Gemini capabilities within applications
  • Exploring Firebase and SDK integration routes
  • Identifying key use cases for embedded AI

Establishing the Development Environment

  • Initializing and configuring Firebase projects
  • Setting up and adjusting Vertex AI SDKs
  • Practical lab: Preparing the development environment

Integrating Gemini into Applications

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

Processing Multimodal Inputs

  • Capturing and interpreting user inputs (voice, image, text)
  • Designing interactive workflows using Gemini
  • Practical lab: Implementing a multimodal input feature

Application Deployment and Monitoring

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

Security and Compliance Frameworks

  • Adopting best practices for data management in AI features
  • Ensuring user privacy and consent mechanisms within apps
  • Practical lab: Securing an AI-integrated feature

Case Studies and Industry Best Practices

  • Reviewing Gemini implementations in consumer and enterprise sectors
  • Analyzing insights from real-world deployments
  • Adopting best practices for scalable in-app AI features

Recap and Future Directions

Requirements

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

Target Audience

  • Mobile application developers
  • Web developers
  • Product teams

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