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Course Outline

Introduction to Vertex AI for Mobile & Web Apps

  • Overview of Gemini’s capabilities within application contexts
  • Integration pathways using Firebase and SDKs
  • Key use cases for embedded AI solutions

Establishing the Development Environment

  • Configuring Firebase projects
  • Installing and setting up Vertex AI SDKs
  • Hands-on lab: Initial environment configuration

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Incorporating text, image, and audio processing capabilities
  • Hands-on lab: Developing a Gemini-driven feature

Processing Multimodal Inputs

  • Capturing and processing diverse user inputs (voice, images, text)
  • Designing interactive workflows powered by Gemini
  • Hands-on lab: Implementing a multimodal input feature

Application Deployment and Monitoring

  • Releasing AI-enabled apps to production
  • Tracking performance and usage metrics via Firebase
  • Hands-on lab: Deployment and testing protocols

Security and Compliance Perspectives

  • Best practices for data management in AI features
  • Managing user privacy and consent mechanisms
  • Hands-on lab: Securing AI functionalities

Case Studies and Industry Best Practices

  • Real-world examples of Gemini in consumer and enterprise sectors
  • Key insights from practical implementations
  • Strategies for scalable AI feature development

Summary and Next Steps

Requirements

  • Foundational programming proficiency in JavaScript, Kotlin, or Swift
  • Working knowledge of mobile or web application development
  • Experience with Firebase or other cloud SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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