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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini's capabilities within application contexts
  • Integration paths using Firebase and SDKs
  • Key use cases for embedded artificial intelligence

Configuring the Development Environment

  • Initiating and configuring Firebase projects
  • Installation and configuration of Vertex AI SDKs
  • Practical lab: Setting up 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 feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs such as voice, images, and text
  • Designing interactive application workflows utilizing Gemini
  • Practical lab: Implementing a multimodal input feature

Application Deployment and Monitoring

  • Releasing AI-driven applications to production environments
  • Tracking performance and usage metrics via Firebase
  • Practical lab: Deploying and testing application builds

Security and Compliance Protocols

  • Best practices for handling data in AI-enabled features
  • Ensuring user privacy and managing consent within applications
  • Practical lab: Securing an AI-driven feature

Case Studies and Best Practices

  • Real-world examples of Gemini in consumer and enterprise applications
  • Insights gained from practical implementation scenarios
  • Strategies for building scalable AI features within apps

Conclusion and Recommendations for Next Steps

Requirements

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

Target Audience

  • Mobile application developers
  • Web developers
  • Product management and development teams

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