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⇱ Gemini and Vertex AI: Building Intelligent Applications | Coursera


Gemini and Vertex AI: Building Intelligent Applications

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Gemini and Vertex AI: Building Intelligent Applications

Instructor: Edureka

Included with

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain Gemini’s multimodal architecture and use AI Studio/APIs for text, vision, and code.

  • Use Gemini APIs and Google AI Studio to explore code generation, document analysis, and multimodal capabilities.

  • Explore Vertex AI foundations, model tuning, and deployment strategies conceptually, with practical demos focused on Gemini.

Details to know

Shareable certificate

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Assessments

17 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Generative AI for Software Engineers & Developers Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

This course introduces the essentials of Gemini AI and Vertex AI, blending architectural insights with hands-on coding, multimodal development, and intelligent agent creation. Designed to give you both theoretical foundations and practical experience, it explores how Google’s most advanced AI systems are transforming software development, data analysis, and real-world applications.

Through guided lessons and demonstrations, you’ll learn to work with Gemini’s multimodal architecture, leverage APIs for text and vision, build smarter apps with AI-driven code generation, and design intelligent agents on Vertex AI. You will also explore advanced model tuning, grounding techniques, and deployment strategies to create reliable, production-ready AI solutions. By the end of this course, you will be able to: • Understand Gemini’s multimodal architecture, APIs, and core capabilities. • Implement Gemini for text, vision, and code tasks, including function calling and document understanding. • Apply prompt engineering strategies and best practices for code generation, optimization, and testing. • Develop multimodal applications using Gemini Live API and natural language-to-database techniques. • Explore Vertex AI foundations, model garden, and Google’s foundation models (Gemini, Imagen, Veo). • Build and enhance intelligent agents with the Agent Development Kit and task-specific prompt guidance. • Tune, evaluate, and optimize Gemini and Vertex AI models using LoRA, QLoRA, and evaluation metrics. • Deploy AI systems with strategies to balance cost, latency, throughput, and performance. This course is ideal for developers, data scientists, and AI practitioners who want to build next-generation applications powered by Google Gemini AI and Vertex AI. A basic understanding of Python and machine learning will be helpful, but no prior experience with Gemini or Vertex AI is required. Join us to explore the cutting edge of multimodal AI and discover how to build smarter, more reliable applications with Gemini and Vertex AI!

Learn Gemini’s multimodal architecture, differences between Pro and Ultra models, and how it compares with other LLMs. Explore the evolution of multimodal AI, Google AI Studio, and setting up the Gemini API for text and vision. Gain skills in structured outputs, function calling, document understanding, grounding, contextual anchoring, and fine-tuning models.

What's included

12 videos5 readings3 assignments2 discussion prompts

12 videosTotal 62 minutes
  • Specialization Introduction7 minutes
  • Course Introduction5 minutes
  • Gemini's Multimodal Architecture6 minutes
  • Gemini Pro Vs. Ultra Model4 minutes
  • Comparison with Other LLMs5 minutes
  • Demonstration: Google AI Studio Interface4 minutes
  • Demonstration: Gemini API for Text and Vision Capabilities7 minutes
  • Understanding AI Studio's Output Capabilities5 minutes
  • Implementing Function Calling5 minutes
  • Document Understanding4 minutes
  • Grounding Techniques in Gemini6 minutes
  • Fine-Tuning Models3 minutes
5 readingsTotal 75 minutes
  • Course Overview15 minutes
  • The Evolution of Multimodal AI: Bridging Text, Image, and Beyond15 minutes
  • Setting Up Gemini API15 minutes
  • Improving AI Reliability with Grounding and Contextual Anchoring15 minutes
  • Module Summary: Gemini AI Essentials: Architecture, APIs & Core Capabilities15 minutes
3 assignmentsTotal 42 minutes
  • Practice Quiz: Understanding Gemini's Architecture6 minutes
  • Practice Quiz: Gemini Code Capabilities6 minutes
  • Knowledge Check: Gemini AI Essentials: Architecture, APIs & Core Capabilities30 minutes
2 discussion promptsTotal 10 minutes
  • Introduce Yourself5 minutes
  • Staying Grounded with Gemini5 minutes

Discover Gemini’s code generation capabilities with multilingual support, best practices, and AI-powered coding assistance. Learn prompt engineering in Google AI Studio for testing, optimization, and regex mastery. Explore multimodal development with OpenAI compatibility, natural language to SQL, document analysis, real-time streaming APIs, and starter apps.

What's included

17 videos3 readings4 assignments3 discussion prompts

17 videosTotal 63 minutes
  • Understanding Code Generation Capabilities6 minutes
  • Demonstration: Python with Gemini on Google AI Studio4 minutes
  • Demonstration: C++ with Gemini on Google AI Studio3 minutes
  • Best Practices for Code Generation2 minutes
  • Prompt Engineering in Google AI Studio3 minutes
  • Unit Testing for Reliable Code3 minutes
  • Demonstartion: Unit Testing for Reliable Code2 minutes
  • Code Optimization Made Easy4 minutes
  • Demonstartion: Code Optimization Made Easy4 minutes
  • Mastering Regex for Developers3 minutes
  • Demonstration: Mastering Regex for Developers4 minutes
  • OpenAI Compatibility3 minutes
  • Natural Language to SQL2 minutes
  • Demonstration: Document Analysis and Code Extraction3 minutes
  • Demonstration: Gemini Live API5 minutes
  • Stream Realtime/ Multimodal Live API4 minutes
  • Demonstration: Starter Apps8 minutes
3 readingsTotal 45 minutes
  • AI-Powered Code Assistance: From Autocomplete to Intelligent Pair Programming15 minutes
  • Converting Natural Language to Database Queries: Challenges and Innovations15 minutes
  • Module Summary: Building Smarter Apps with Gemini AI: Code, Prompts & Multimodal Power15 minutes
4 assignmentsTotal 48 minutes
  • Practice Quiz: Code Generation with Gemini6 minutes
  • Practice Quiz: Prompt Engineering with Gemini6 minutes
  • Practice Quiz: Multimodal Development with Gemini6 minutes
  • Knowledge Check: Building Smarter Apps with Gemini AI: Code, Prompts & Multimodal Power30 minutes
3 discussion promptsTotal 15 minutes
  • Writing Code the Right Way5 minutes
  • Testing for Trust5 minutes
  • Talking to Databases5 minutes

Set up your Vertex AI environment, explore Model Garden, and examine Google’s foundation models like Gemini, Imagen, and Veo. Develop intelligent agents using the Agent Development Kit and Agent Engine, enhance them with tools, and apply task-specific prompts. Leverage generative AI for text, code, image, and video generation, and utilize grounding, translation, and AI-powered prompt writing tools.

What's included

10 videos6 readings4 assignments3 discussion prompts

10 videosTotal 49 minutes
  • Introduction to Vertex AI6 minutes
  • Model Garden Exploration and Usage on Vertex AI5 minutes
  • Demonstration: Getting Started with Google Cloud: Login & Billing Setup3 minutes
  • Introduction to AI Agents on Vertex AI5 minutes
  • Developing Agents with ADK5 minutes
  • Prompting for Effective Agent6 minutes
  • Text and Code Generation with Vertex AI3 minutes
  • Mastering Image and Video Generation4 minutes
  • Grounding and Translation Capabilities6 minutes
  • Utilizing AI-Powered Prompt Writing Tools and Tokenizers6 minutes
6 readingsTotal 90 minutes
  • Setting Up Your Vertex AI Development Environment15 minutes
  • Introduction to Google's Foundation Models: Gemini, Imagen, and Veo15 minutes
  • Agent Tools: Enhancing Agent Capabilities15 minutes
  • Build a Simple Knowledge Chat Agent with Vertex AI15 minutes
  • Analyzing Data with Generative AI Models15 minutes
  • Module Summary: Vertex AI Foundations & Intelligent Agent Development15 minutes
4 assignmentsTotal 48 minutes
  • Practice Quiz: Discovering Vertex AI6 minutes
  • Practice Quiz: Building Intelligent Agents on Vertex AI6 minutes
  • Practice Quiz: Harnessing the Capabilities of Generative AI Models6 minutes
  • Knowledge Check: Vertex AI Foundations & Intelligent Agent Development30 minutes
3 discussion promptsTotal 15 minutes
  • Building the Right Setup5 minutes
  • Building with ADK and Agent Engine5 minutes
  • Staying Grounded and Translated5 minutes

Apply tuning techniques to optimize Gemini, Imagen, and translation models, implement LoRA and QLoRA for efficiency, and migrate seamlessly from Google AI to Vertex AI using OpenAI libraries. Conduct evaluations with the Python SDK, define metrics, analyze results, and customize judge models for improved accuracy. Deploy generative AI models with scalable strategies, optimize cost, latency, and performance, and enhance efficiency through caching, batch inference, and throughput management.

What's included

11 videos4 readings4 assignments3 discussion prompts

11 videosTotal 49 minutes
  • Introduction to Model Tuning5 minutes
  • Fine-Tuning Gemini Models for Optimal Performance4 minutes
  • Tuning Embeddings, Imagen, and Translation Models4 minutes
  • Migrating from Google AI to Vertex AI Using the OpenAI Library5 minutes
  • Introduction to Model Evaluation on Vertex AI5 minutes
  • Defining Evaluation Metrics and Preparing Your Dataset4 minutes
  • Running and Interpreting Model Evaluation Results4 minutes
  • Customizing Judge Models for Enhanced Evaluation4 minutes
  • Model Deployment Strategies and Provisioned Throughput4 minutes
  • Optimizing Cost, Latency, and Performance in AI Systems6 minutes
  • Efficiency Boost: Caching, Batching and Throughput3 minutes
4 readingsTotal 60 minutes
  • Tuning Recommendations with LoRA and QLoRA15 minutes
  • Fine-Tuning a Small Text Model in Vertex AI15 minutes
  • Performing Evaluation with the Python SDK15 minutes
  • Module Summary: Advanced Model Tuning, Evaluation & Deployment on Vertex AI15 minutes
4 assignmentsTotal 48 minutes
  • Practice Quiz: Model Tuning and Optimization on Vertex AI6 minutes
  • Practice Quiz: Evaluating and Customizing Generative AI Models6 minutes
  • Practice Quiz: Deploying Generative AI Models for Production6 minutes
  • Knowledge Check: Advanced Model Tuning, Evaluation & Deployment on Vertex AI30 minutes
3 discussion promptsTotal 15 minutes
  • Beyond Text: Imagen and Translation5 minutes
  • Metrics That Matter5 minutes
  • Balancing the Trade-offs5 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz.

What's included

1 video2 assignments1 discussion prompt

1 videoTotal 4 minutes
  • Course Summary4 minutes
2 assignmentsTotal 90 minutes
  • End Course Knowledge Check: Gemini and Vertex AI: Building Intelligent Applications60 minutes
  • Building a Multimodal AI-Powered Healthcare Assistant with Gemini and Vertex AI30 minutes
1 discussion promptTotal 5 minutes
  • Describe Your Learning Journey5 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka
211 Courses190,189 learners

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Frequently asked questions

A practical introduction to Gemini AI using Google AI Studio and GCP for multimodal AI and code generation.

Developers, data scientists, and tech professionals seeking hands-on Gemini demos and practical workflows.

No; basic Python or ML familiarity is helpful but not required.

Gemini APIs, Google AI Studio, and select Google Cloud Platform (GCP) services.

Primarily Python, with patterns transferable to other languages.

Yes, working with text, images, and code via Gemini’s multimodal capabilities.

Confidence using Gemini for practical demos, cleaner prompts and structured outputs, and a clear conceptual view of Vertex AI.

A modern browser, a Google account, and basic Python tools are sufficient however, GCP is recommended.

Yes, short knowledge checks and guided exercises after each section.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.