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Gemini API with VertexAI for Developers

Published by Pearson

Intermediate content levelIntermediate

Build and deploy real-world generative AI applications with Gemini API, Vertex AI, and GCP

Course Outcomes:

  • Gain a deep understanding of Google’s unified Generative AI ecosystem.
  • Focus on practical implementation of multimodal AI applications.
  • Learn best practices for end-to-end Gemini-powered AI applications.

This live training provides an excellent opportunity to explore Google's cutting-edge Generative AI capabilities, specifically focusing on the Gemini API within the Vertex AI framework on Google Cloud Platform. As AI development accelerates, understanding and implementing multimodal generative models have become essential for creating innovative and intelligent applications. The session will provide participants with the knowledge and hands-on skills to leverage generative AI across various modalities, including text generation for content creation and natural language processing, image synthesis for design and visual communication, video creation for automated production and immersive experiences, and audio generation for advancements in voice assistants and sound design.

This live training is designed to provide developers with the necessary skills and knowledge to integrate powerful AI features into their projects. Whether building new applications or enhancing existing ones, the insights gained will enable the creation of more dynamic, interactive, and intelligent user experiences. By mastering the Gemini API within Vertex AI and the broader GCP ecosystem, developers will be positioned to harness the latest developments by one of the forefront leaders in this growing field.

What you’ll learn and how you can apply it

  • Create Custom Tools to fetch stock data, read/write files, or search the web.
  • Implement both linear workflows into hierarchical structures where a "Manager" agent autonomously plans and delegates work.
  • Abstract agent prompts and definitions into configuration files for easier testing and version control.

This live event is for you because...

  • You are a software developer or an AI engineer who is looking to integrate advanced generative AI capabilities into your application
  • You are an intermediate learner who wants to move beyond theoretical knowledge and gain practical implementation skills with Google's Generative AI offerings.

Prerequisites

  • Basic Python
  • Numpy
  • Matplotlib
  • Jupyter

Course Set-up

  • Python
  • Pandas
  • Matplotlib
  • Jupyter

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 – Gemini API and Vertex AI Fundamentals (50 minutes)

  • Generative AI
  • Introduction to Gemini API
  • Overview of Vertex AI

Q&A + Break (10 minutes)

Segment 2 – Gemini API for Text Generation (40 minutes)

  • Prompt Engineering for Text Models
  • Text Generation with Gemini API
  • Text Models for Information Extraction

Q&A + Break (10 minutes)

Segment 3 – Multimodal Applications with Gemini API (30 minutes)

  • Exploring Gemini's Image and Video Understanding Capabilities
  • Hands-on: Image Captioning and Visual Question Answering
  • Integrating Multimodal Inputs for Advanced Applications
  • Real-world Use Cases for Multimodal Generative AI

Q&A + Break (10 minutes)

Segment 4 – Vertex AI and Deployment Strategies (50 minutes)

  • Advanced MLOps Practices with Vertex AI
  • Model Monitoring and Explainability on Vertex AI
  • Deploying Gemini-powered Applications
  • Scaling and Optimization for Production Environments

Q&A + Break (10 minutes)

Segment 5 – End-to-End Generative AI Solutions (30 minutes)

  • Designing and Architecting Generative AI Systems
  • Best Practices for Security and Data Privacy

Your Instructor

  • Bruno Gonçalves

    Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.

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Skill covered

Gemini