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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini’s capabilities within app environments
  • Integration pathways using Firebase and SDKs
  • Key use cases for embedded artificial intelligence

Configuring the Development Environment

  • Setting up and configuring Firebase projects
  • Installing and tuning Vertex AI SDKs
  • Practical lab: Initializing the development environment

Integrating Gemini into Application Logic

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

Managing Multimodal Input Streams

  • Capturing and processing diverse user inputs, including voice, image, and text
  • Designing interactive workflows leveraging Gemini
  • Practical lab: Implementing multimodal input features

Application Deployment and Performance Monitoring

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

Addressing Security and Compliance Requirements

  • Best practices for data management in AI-centric features
  • Ensuring user privacy and managing consent mechanisms within apps
  • Practical lab: Securing AI-driven functionality

Case Studies and Industry Best Practices

  • Analyzing Gemini implementations in consumer and enterprise contexts
  • Insights derived from real-world deployment experiences
  • Strategies for building scalable AI features within applications

Wrap-up and Future Directions

Requirements

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

Target Audience

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

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