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Business-Ready AI with Google Vertex AI (Easy and No Code)

Name: Business-Ready AI with Google Vertex AI (Easy and No Code)
Rating: 4.6 (44 reviews)

Turn Ideas into Live AI Tools with Google Vertex AI. Deploy AI Chatbots, RAG Systems & ML Models Without Writing Code

Highest Rated

Created byDiogo Alves de Resende

Last updated 1/2026

English

What you'll learn

Set up a Google Cloud Console account and navigate the Vertex AI environment with confidence.
Build your first AI application using Vertex AI Studio without writing any code.
Create effective system messages, personas, and few-shot examples to control AI behavior.
Identify and prevent jailbreak attempts using built-in safety and prompt-engineering techniques.
Deploy a no-code RAG chatbot using Vertex AI and validate its performane a structured corpus for Retrieval-Augmented Generation (RAG).
Integrate a Vertex AI chatbot into a Lovable-built website or application.
Use AutoML in Vertex AI to train classification models without coding.
Run batch inference jobs to generate predictions at scale.
Train and assess forecasting models for time-series data without writing code.
Interpret model evaluation reports to make reliable business decisions.

Course content

10 sections • 42 lectures • 3h 0m total length

Introduction1:29
Build, train, and deploy business-ready AI with Google Vertex AI using no-code tools to create apps, chatbots, and models, then connect them to documents with Rag engine and AutoML.

Setting Up Google Cloud Console Account3:52
Set up a Google Cloud Console account from scratch on cloud.google.com to access Vertex AI, including creating an organization profile, verifying payment details, and starting the free trial.
Setting up Vertex AI4:54
Trying Veo 3.13:12
Explore vertex ai studio's multimedia tools, including chat, images, video, music, and live, and generate eight-second videos from a Dragon Ball prompt with seed and safety options.

Game Plan for Your First AI App1:48
Design your first AI app on Vertex AI without coding by setting the system message, adding personality, tweaking behavior, and testing live to build a custom AI assistant.
Deploy the First App9:47
System Message4:17
Update the App with the System Message4:24
Persona and Few-Shot2:44
Update the App with Few-Shot5:33
Jailbreaking6:32
Explore jailbreaking risks in no-code Vertex AI apps, revealing how system messages, few-shot examples, and data leakage can occur, and learn defensive measures for enterprise AI security.
Tell me Your Secrets3:30
Guess the Character
Introduction to AI

Game Plan for No-Code RAG with Vertex AI2:10
Explore no-code retrieval augmented generation with Vertex AI, using your own documents to ingest, index, retrieve, and generate trustworthy answers that pull exact excerpts through governance and vector databases.
RAG4:15
Rag, or retrieval augmented generation, grounds answers with up-to-date data by retrieving relevant documents, augmenting prompts, and generating grounded responses across healthcare, finance, education, and more.
Creating the Corpus5:36
Chunking3:23
Embeddings3:45
Embeddings turn words into numbers so computers can search, classify, and power chatbots and document search, with related meanings ending up near each other in context.
Vector stores3:14
Giving Drive Access and Uploading4:48
Deploying the App4:36
Test and validate your Rag Engine corpus in Vertex AI Studio, inspect the restaurant data, and deploy the app to overcome quota limits and resource exhaustion.
Debugging RAG4:06
Debugging RAG in Google Vertex AI without code by configuring storage roles, choosing supported regions for rack engine, and testing prompts to generate recommendations and deploy mini web apps.
Improving RAG3:38
Improve your rag by applying system instructions and role constraints for concise, friendly answers, using documents when unsure. Leverage lovable to build a restaurant bot site with free credits.

Game Plan for Building ML Models1:32
Loading the Data8:07
Explore data sets and training in Google Vertex AI, focusing on tabular data for a smartphone price multi-classification problem, uploading CSV files, creating a dataset, and generating statistics.
Training the Model6:56
Train a new model using Vertex AI AutoML tabular training for price range classification, exploring feature selection, correlation with target, and setting training budgets and early stopping.
What is AutoML?2:16
Classification Model Assessment Part 18:59
Evaluate classification models using accuracy, confusion matrix, precision, recall, and F1, with examples of true and false positives and negatives, and consider ROC AUC for imbalanced datasets.
Classification Model Assessment Part 25:08
Batch Inference7:04
Evaluating the model10:57
Checking the results4:55
Audit Vertex AI predictions with no-code AutoML by exporting results, previewing CSVs, and visualizing predictions. Handle batch inference status and exit-handler errors to ensure reliable, business-ready insights.

Requirements

You only need a Google account to set up Google Cloud.

Description

Vertex AI is Google Cloud’s all-in-one AI platform that lets you build, deploy, and manage machine learning and generative AI applications, all without needing to write code.

With Vertex AI, you get access to AutoML for tabular, image, text, or video data; prebuilt foundation models (like Gemini), vector search, RAG support, chat/agent builders, deployment tools, and scalable infrastructure, all under one roof.

This makes it ideal if you want to go from idea to working AI solution fast.

You don’t need programming skills, just a browser and a Google account.

That lowers the barrier for business users, creators, analysts or entrepreneurs to leverage AI.

Plus: as companies worldwide race to integrate AI, skills in Vertex AI are increasingly in demand.

What You’ll Get Out of This Course (Outcomes)
By the end, you will be able to:

Set up Vertex AI and navigate the console like a pro.
Build your first generative-AI app using prompt engineering — system messages, personas, few-shot, safety.
Deploy chatbots and AI agents using RAG (vector store + embeddings + corpus upload), without code.
Run AutoML (tabular/text/image), train ML models, and deploy them for real-world predictions.
Build time-series forecasting pipelines using Vertex AI (no-code).

Why Learn with Me

I have over 50 000 students on Udemy.

Plus, I bring a unique mix: I’m a data-science educator and course creator, and also a startup founder building real products.

That means this course is built for people who want real business impact, not just academic theory.

You get a clear, practical path from zero skills to deploying real AI tools that solve real problems.

I know what works in real-world settings.

I’ll guide you step-by-step, skip the fluff, and show you how to get working AI fast.

Who this course is for:

Beginners who want to build AI tools without writing code.
Business professionals who want to automate tasks using Google’s AI.
Creators, freelancers, and entrepreneurs who want to add AI features to their projects.
Students and career-changers who want practical AI skills fast.
Anyone curious about Vertex AI and how to build real apps with it.

Business-Ready AI with Google Vertex AI (Easy and No Code)

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 1min

[ACTION] Download the Course Materials1 lecture • 1min

Google Cloud Console Account3 lectures • 12min

Your First AI App with Vertex AI8 lectures • 39min

Will you help me?2 lectures • 1min

No-Code RAG with Vertex AI10 lectures • 40min

About Your Instructor, Diogo1 lecture • 2min

Deploying RAG with LOVABLE6 lectures • 27min

Building ML Models9 lectures • 56min

What's Next?1 lecture • 2min

Requirements

Description

Who this course is for: