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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Apps

  • Overview of Gemini's capabilities within applications
  • Integration pathways for Firebase and SDKs
  • Application scenarios for embedded AI

Configuring the Development Environment

  • Firebase project initialization and configuration
  • Installation and setup of Vertex AI SDKs
  • Practical lab: Environment configuration

Integrating Gemini into Applications

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

Processing Multimodal Inputs

  • Acquiring and processing user inputs (voice, image, text)
  • Designing interactive app workflows utilizing Gemini
  • Practical lab: Multimodal input functionality

Application Deployment and Monitoring

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

Security and Compliance Frameworks

  • Best practices for data management in AI features
  • User privacy and consent protocols within apps
  • Practical lab: Securing an AI feature

Case Studies and Industry Best Practices

  • Case examples of Gemini in consumer and enterprise applications
  • Insights from real-world deployments
  • Strategies for scalable AI features in apps

Conclusions and Subsequent Steps

Requirements

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

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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