Get in Touch

Course Outline

Introduction to Vertex AI for Mobile and Web Applications

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

Configuring the Development Environment

  • Initiating and setting up Firebase projects
  • Installing and adjusting Vertex AI SDK configurations
  • Practical lab: Establishing the development environment

Integrating Gemini into Applications

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

Processing Multimodal Inputs

  • Capturing and interpreting user data across voice, image, and text formats
  • Designing interactive workflows driven by Gemini
  • Practical lab: Implementing multimodal input features

Deploying and Monitoring Applications

  • Releasing AI-integrated applications to production
  • Tracking performance and utilization metrics via Firebase
  • Practical lab: Deployment and application testing

Addressing Security and Compliance

  • Applying best practices for data management in AI features
  • Ensuring user privacy and consent protocols within apps
  • Practical lab: Securing AI-based functionalities

Real-World Case Studies and Best Practices

  • Examining Gemini implementations in consumer and enterprise sectors
  • Deriving insights from real-world project outcomes
  • Adopting best practices for scalable in-app AI features

Conclusion and Future Pathways

Requirements

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

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories