Get in Touch

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Understanding Gemini's role and capabilities within apps
  • Exploring Firebase and SDK integration strategies
  • Identifying key use cases for embedded AI

Configuring the Development Environment

  • Initial setup and configuration of Firebase projects
  • Installation and configuration of Vertex AI SDKs
  • Practical lab: Establishing the development environment

Integrating Gemini into Applications

  • Making Gemini API calls from client-side applications
  • Implementing text, image, and audio processing capabilities
  • Practical lab: Developing a core Gemini-powered feature

Processing Multimodal Inputs

  • Capturing and interpreting diverse user inputs (voice, image, text)
  • Designing interactive workflows centered around Gemini
  • Practical lab: Building a multimodal input feature

Deployment and Performance Monitoring

  • Releasing AI-enhanced applications to production
  • Tracking performance metrics and usage via Firebase
  • Practical lab: Deploying and validating application functionality

Security and Compliance Protocols

  • Adopting best practices for AI data handling
  • Ensuring user privacy and managing consent within apps
  • Practical lab: Implementing security measures for AI features

Case Studies and Industry Best Practices

  • Analyzing Gemini implementations in consumer and enterprise sectors
  • Extracting insights from real-world deployment experiences
  • Applying best practices for scalable AI features in applications

Course Conclusion and Recommended Next Steps

Requirements

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

Target Audience

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

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories