Description

Introduction

Vertex AI is Google Cloud’s fully managed machine learning (ML) platform that simplifies building, training, and deploying models. Whether you’re an analyst, developer, or new data scientist, Vertex AI provides accessible tools—from no-code AutoML to Python-based notebooks—allowing anyone to create models and predictions with ease. This guide walks beginners through the complete process, from setup to deployment.

Prerequisites

• A Google Cloud account with billing enabled

• Basic understanding of data formats (CSV, JSON)

• Familiarity with web interfaces and cloud consoles

• (Optional) Introductory knowledge of ML terms like training, model, prediction

Table of Contents

1. Introduction to Vertex AI
   1.1 What is Vertex AI?
   1.2 Key Features and Components
   1.3 Role of AutoML and Custom Training
   1.4 Beginner vs. Advanced Use Cases

2. Setting Up Your Google Cloud Environment
   2.1 Creating a GCP Project
   2.2 Enabling Vertex AI and Required APIs
   2.3 Creating a Cloud Storage Bucket
   2.4 Assigning IAM Permissions (Editor, Vertex AI User)
   2.5 Understanding Regions and Quotas

3. Understanding the Vertex AI Console
   3.1 Navigating the Dashboard
   3.2 Overview of Tabs: Datasets, Models, Endpoints, Pipelines
   3.3 Understanding the Workflow and Resources

4. Preparing and Uploading Your Dataset
   4.1 Dataset Requirements and Formats
   4.2 Creating a Dataset in Vertex AI
   4.3 Uploading Data from Local or Cloud Storage
   4.4 Data Labeling Basics and Built-in Labeling Tools

5. Training Models with AutoML
   5.1 Choosing Model Type: Tabular, Image, Text
   5.2 Starting a No-Code Training Job
   5.3 Reviewing Training Logs and Status
   5.4 Viewing Evaluation Metrics (Accuracy, Precision, Recall)
   5.5 Exporting and Versioning Trained Models

6. Deploying and Testing a Model
   6.1 Creating a Deployment Endpoint
   6.2 Deploying the Model to the Endpoint
   6.3 Sending a Sample Prediction Request
   6.4 Testing via Console and Python Client

7. Introduction to Notebooks (Vertex AI Workbench)
   7.1 What is a Jupyter Notebook?
   7.2 Launching a Pre-built Notebook Instance
   7.3 Running a Python-based ML Example
   7.4 Using TensorFlow or Scikit-learn in Vertex

8. Monitoring, Logging, and Resource Cleanup
   8.1 Viewing Logs in Cloud Logging
   8.2 Monitoring Deployment Latency and Errors
   8.3 Stopping and Deleting Endpoints and Models
   8.4 Cost Optimization and Budget Alerts

9. Exploring Pre-trained APIs and GenAI Tools
   9.1 Using Vision, Text, and Translation APIs
   9.2 Introduction to Vertex AI GenAI Studio
   9.3 Trying Gemini or PaLM-based Text Generation
   9.4 Comparing Pre-trained vs. Custom Models

10. Tips, Resources, and Career Pathways
    10.1 Google Cloud Training Resources
    10.2 Qwiklabs and Skill Boost Labs
    10.3 Career Paths: ML Engineer, Cloud Support, Data Analyst
    10.4 Community, Forums, and Certifications

Google Vertex AI makes machine learning more accessible to beginners through AutoML, intuitive UI, and integrated tools. It helps users go from raw data to real-time predictions without deep expertise.
This guide enables you to take your first steps confidently, while giving you a pathway to progress into more advanced ML development and career growth in cloud AI.