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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • An overview of Gemini’s capabilities within application contexts.
  • Exploring integration pathways with Firebase and SDKs.
  • Identifying key use cases for embedded AI solutions.

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 Applications

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

Managing Multimodal Inputs

  • Capturing and processing various user inputs, including voice, images, and text.
  • Designing interactive workflows that leverage Gemini.
  • Practical lab: Implementing a multimodal input feature.

Application Deployment and Monitoring

  • Releasing AI-enabled applications to production.
  • Tracking performance and usage metrics via Firebase.
  • Practical lab: Deploying and testing application instances.

Addressing Security and Compliance

  • Implementing best practices for data management in AI features.
  • Ensuring user privacy and obtaining consent within applications.
  • Practical lab: Securing AI-driven functionality.

Case Studies and Industry Best Practices

  • Examining examples of Gemini usage in consumer and enterprise applications.
  • Learning from real-world implementation challenges and solutions.
  • Adopting best practices for building scalable in-app AI features.

Conclusion and Future Recommendations

Requirements

  • Foundational programming skills in JavaScript, Kotlin, or Swift.
  • A solid understanding of mobile or web application development workflows.
  • Prior experience working with Firebase or other cloud-based SDKs.

Target Audience

  • Mobile application developers.
  • Web developers.
  • Product engineering teams.

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