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Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini's capabilities within apps
  • Firebase and SDK integration strategies
  • Key use cases for embedded AI solutions

Preparing the Development Environment

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

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Combining text, image, and audio features
  • Practical lab: developing a Gemini-driven feature

Managing Multimodal Inputs

  • Capturing and processing user data (voice, image, text)
  • Designing interactive workflows with Gemini
  • Practical lab: implementing multimodal input capabilities

Application Deployment and Monitoring

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

Security and Compliance Essentials

  • Best practices for data management in AI features
  • Ensuring user privacy and consent mechanisms
  • Practical lab: securing AI components

Case Studies and Industry Best Practices

  • Examples of Gemini in consumer and enterprise applications
  • Insights from real-world deployment scenarios
  • Strategies for building scalable in-app AI features

Recap and Future Directions

Requirements

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

Intended Audience

  • Mobile developers
  • Web developers
  • Product management and engineering teams
 14 Hours

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