Gemini and Vertex AI: Building Intelligent Applications
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Gemini and Vertex AI: Building Intelligent Applications
This course is part of Generative AI for Software Engineers & Developers Specialization
Included with
Recommended experience
Recommended experience
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.
Skills you'll gain
- Artificial Intelligence and Machine Learning (AI/ML)
- Cloud Platforms
- Software Development Tools
- Prompt Patterns
- LLM Application
- Google Cloud Platform
- Application Deployment
- Generative Model Architectures
- Fine-tuning
- Large Language Modeling
- Artificial Intelligence
- Generative AI Agents
- AI Personalization
- Model Optimization
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- 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 videos•Total 62 minutes
- Specialization Introduction•7 minutes
- Course Introduction•5 minutes
- Gemini's Multimodal Architecture•6 minutes
- Gemini Pro Vs. Ultra Model•4 minutes
- Comparison with Other LLMs•5 minutes
- Demonstration: Google AI Studio Interface•4 minutes
- Demonstration: Gemini API for Text and Vision Capabilities•7 minutes
- Understanding AI Studio's Output Capabilities•5 minutes
- Implementing Function Calling•5 minutes
- Document Understanding•4 minutes
- Grounding Techniques in Gemini•6 minutes
- Fine-Tuning Models•3 minutes
5 readings•Total 75 minutes
- Course Overview•15 minutes
- The Evolution of Multimodal AI: Bridging Text, Image, and Beyond•15 minutes
- Setting Up Gemini API•15 minutes
- Improving AI Reliability with Grounding and Contextual Anchoring•15 minutes
- Module Summary: Gemini AI Essentials: Architecture, APIs & Core Capabilities•15 minutes
3 assignments•Total 42 minutes
- Practice Quiz: Understanding Gemini's Architecture•6 minutes
- Practice Quiz: Gemini Code Capabilities•6 minutes
- Knowledge Check: Gemini AI Essentials: Architecture, APIs & Core Capabilities•30 minutes
2 discussion prompts•Total 10 minutes
- Introduce Yourself•5 minutes
- Staying Grounded with Gemini•5 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 videos•Total 63 minutes
- Understanding Code Generation Capabilities•6 minutes
- Demonstration: Python with Gemini on Google AI Studio•4 minutes
- Demonstration: C++ with Gemini on Google AI Studio•3 minutes
- Best Practices for Code Generation•2 minutes
- Prompt Engineering in Google AI Studio•3 minutes
- Unit Testing for Reliable Code•3 minutes
- Demonstartion: Unit Testing for Reliable Code•2 minutes
- Code Optimization Made Easy•4 minutes
- Demonstartion: Code Optimization Made Easy•4 minutes
- Mastering Regex for Developers•3 minutes
- Demonstration: Mastering Regex for Developers•4 minutes
- OpenAI Compatibility•3 minutes
- Natural Language to SQL•2 minutes
- Demonstration: Document Analysis and Code Extraction•3 minutes
- Demonstration: Gemini Live API•5 minutes
- Stream Realtime/ Multimodal Live API•4 minutes
- Demonstration: Starter Apps•8 minutes
3 readings•Total 45 minutes
- AI-Powered Code Assistance: From Autocomplete to Intelligent Pair Programming•15 minutes
- Converting Natural Language to Database Queries: Challenges and Innovations•15 minutes
- Module Summary: Building Smarter Apps with Gemini AI: Code, Prompts & Multimodal Power•15 minutes
4 assignments•Total 48 minutes
- Practice Quiz: Code Generation with Gemini•6 minutes
- Practice Quiz: Prompt Engineering with Gemini•6 minutes
- Practice Quiz: Multimodal Development with Gemini•6 minutes
- Knowledge Check: Building Smarter Apps with Gemini AI: Code, Prompts & Multimodal Power•30 minutes
3 discussion prompts•Total 15 minutes
- Writing Code the Right Way•5 minutes
- Testing for Trust•5 minutes
- Talking to Databases•5 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 videos•Total 49 minutes
- Introduction to Vertex AI•6 minutes
- Model Garden Exploration and Usage on Vertex AI•5 minutes
- Demonstration: Getting Started with Google Cloud: Login & Billing Setup•3 minutes
- Introduction to AI Agents on Vertex AI•5 minutes
- Developing Agents with ADK•5 minutes
- Prompting for Effective Agent•6 minutes
- Text and Code Generation with Vertex AI•3 minutes
- Mastering Image and Video Generation•4 minutes
- Grounding and Translation Capabilities•6 minutes
- Utilizing AI-Powered Prompt Writing Tools and Tokenizers•6 minutes
6 readings•Total 90 minutes
- Setting Up Your Vertex AI Development Environment•15 minutes
- Introduction to Google's Foundation Models: Gemini, Imagen, and Veo•15 minutes
- Agent Tools: Enhancing Agent Capabilities•15 minutes
- Build a Simple Knowledge Chat Agent with Vertex AI•15 minutes
- Analyzing Data with Generative AI Models•15 minutes
- Module Summary: Vertex AI Foundations & Intelligent Agent Development•15 minutes
4 assignments•Total 48 minutes
- Practice Quiz: Discovering Vertex AI•6 minutes
- Practice Quiz: Building Intelligent Agents on Vertex AI•6 minutes
- Practice Quiz: Harnessing the Capabilities of Generative AI Models•6 minutes
- Knowledge Check: Vertex AI Foundations & Intelligent Agent Development•30 minutes
3 discussion prompts•Total 15 minutes
- Building the Right Setup•5 minutes
- Building with ADK and Agent Engine•5 minutes
- Staying Grounded and Translated•5 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 videos•Total 49 minutes
- Introduction to Model Tuning•5 minutes
- Fine-Tuning Gemini Models for Optimal Performance•4 minutes
- Tuning Embeddings, Imagen, and Translation Models•4 minutes
- Migrating from Google AI to Vertex AI Using the OpenAI Library•5 minutes
- Introduction to Model Evaluation on Vertex AI•5 minutes
- Defining Evaluation Metrics and Preparing Your Dataset•4 minutes
- Running and Interpreting Model Evaluation Results•4 minutes
- Customizing Judge Models for Enhanced Evaluation•4 minutes
- Model Deployment Strategies and Provisioned Throughput•4 minutes
- Optimizing Cost, Latency, and Performance in AI Systems•6 minutes
- Efficiency Boost: Caching, Batching and Throughput•3 minutes
4 readings•Total 60 minutes
- Tuning Recommendations with LoRA and QLoRA•15 minutes
- Fine-Tuning a Small Text Model in Vertex AI•15 minutes
- Performing Evaluation with the Python SDK•15 minutes
- Module Summary: Advanced Model Tuning, Evaluation & Deployment on Vertex AI•15 minutes
4 assignments•Total 48 minutes
- Practice Quiz: Model Tuning and Optimization on Vertex AI•6 minutes
- Practice Quiz: Evaluating and Customizing Generative AI Models•6 minutes
- Practice Quiz: Deploying Generative AI Models for Production•6 minutes
- Knowledge Check: Advanced Model Tuning, Evaluation & Deployment on Vertex AI•30 minutes
3 discussion prompts•Total 15 minutes
- Beyond Text: Imagen and Translation•5 minutes
- Metrics That Matter•5 minutes
- Balancing the Trade-offs•5 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 video•Total 4 minutes
- Course Summary•4 minutes
2 assignments•Total 90 minutes
- End Course Knowledge Check: Gemini and Vertex AI: Building Intelligent Applications•60 minutes
- Building a Multimodal AI-Powered Healthcare Assistant with Gemini and Vertex AI•30 minutes
1 discussion prompt•Total 5 minutes
- Describe Your Learning Journey•5 minutes
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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.
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