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Course Outline

Introduction to Vertex AI for Mobile & 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

  • Establishing and configuring Firebase projects
  • 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 functionalities
  • Practical lab: Developing a Gemini-powered feature

Managing Multimodal Inputs

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

Application Deployment and Monitoring

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

Security and Compliance Implications

  • Best practices for data handling in AI features
  • User privacy and consent management in applications
  • Practical lab: Securing AI-driven features

Case Studies and Industry Best Practices

  • Examples of Gemini usage in consumer and enterprise apps
  • Insights from real-world implementations
  • Best practices for building scalable in-app AI features

Wrap-Up and Future Steps

Requirements

  • Foundational programming skills in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development principles
  • Prior experience utilizing Firebase or cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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