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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Exploring Gemini's capabilities within application contexts
  • Understanding integration pathways through Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and configuring Firebase projects
  • Installing and setting up Vertex AI SDKs
  • Hands-on lab: Initializing the development environment

Integrating Gemini into Applications

  • Executing Gemini API calls from client-side applications
  • Incorporating text, image, and audio processing capabilities
  • Hands-on lab: Developing a Gemini-enabled feature

Managing Multimodal Input

  • Capturing and processing diverse user inputs, including voice, images, and text
  • Designing interactive application workflows driven by Gemini
  • Hands-on lab: Implementing multimodal input functionalities

Application Deployment and Monitoring

  • Deploying AI-enhanced applications to production environments
  • Tracking performance and usage metrics via Firebase
  • Hands-on lab: Deploying and testing application releases

Security and Compliance Frameworks

  • Implementing best practices for data handling in AI features
  • Ensuring user privacy and consent compliance within apps
  • Hands-on lab: Securing AI-driven functionalities

Case Studies and Industry Best Practices

  • Examining Gemini implementations in consumer and enterprise applications
  • Analyzing insights gained from real-world deployment scenarios
  • Adopting best practices for scalable in-app AI features

Summary and Future Directions

Requirements

  • Fundamental programming proficiency in JavaScript, Kotlin, or Swift
  • Proficiency in mobile or web application development
  • Practical experience with Firebase or other cloud SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product development teams

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