Vertex AI Fundamentals Training in Astana, Kazakhstan

This Vertex AI training provides a practical introduction to Google Cloud’s end-to-end AI platform. Participants learn how to build, deploy, and experiment with machine learning and generative AI models using no-code and low-code tools. The program is designed for learners with minimal or no prior experience in AI, ensuring concepts are explained simply and hands-on exercises reinforce learning. By the end of the training, participants will be able to navigate the Vertex AI workspace, create AutoML models, deploy them, and experiment with Gemini generative AI models using Vertex AI Studio.

Foundations & Hands-On Basics

Module 1: Introduction to Google Cloud & Vertex AI

• What is Google Cloud?
• What is Vertex AI?
• Why use Vertex AI?
• Types of AI workloads supported (ML, Generative AI)
• Vertex AI vs AWS & Azure (simple comparison)

Module 2: Navigating the Vertex AI Workspace

• Vertex AI Console overview
• Understanding:
• Datasets
• Models
• Notebooks (Workbench)
• Endpoints
• IAM basics (simple explanation)
• Storing files in Cloud Storage (GCS)

Module 3: Data Preparation for Beginners

• Importing data into Vertex AI
• Uploading files to Cloud Storage
• Basic labeling & dataset creation
• What is a Feature Store?

Module 4: AutoML – Build ML Models Without Coding

• What is AutoML?
• AutoML for:
• Tabular data
• Text classification
• Image classification
• Training an AutoML model (guided demo)
• Understanding model metrics (accuracy, precision, recall)
• Best practice

Hands-On Labs

• Create a GCP project
• Upload data to Cloud Storage
• Build a simple AutoML Image/Text/Tabular model
• Evaluate the model
• Generate predictions from the console

Deployments, Generative AI & Simple Applications

Module 5: Model Deployment

• What is an Endpoint?
• Deploying a model with 1 click
• Online vs batch prediction
• Cost basics
• Testing predictions with the UI

Module 6: Introduction to Generative AI (Gemini) on Vertex AI

• What is Gemini?
• Text, chat, and multimodal models
• Understanding prompts
• Simple prompt engineering techniques
• Safety settings overview

Module 7: Embeddings & Very Simple RAG

• What are embeddings?
• What is vector search?
• When to use RAG
• Simple workflow: Upload → Embed → Query

Module 8: Using Vertex AI with No-Code & Low-Code Tools

• Using Vertex AI Studio (Playground)
• BigQuery integration
• Using prebuilt Google templates
• Introduction to MakerSuite (optional)

Module 9: Intro to Monitoring & Governance

• Why monitoring matters
• What is drift? Simple explanation
• Responsible AI basics
• Role-based access (simple IAM overview)

Hands-On Labs

• Deploy a model to an endpoint
• Try predictions in the UI
• Use Vertex AI Studio to test Gemini prompts
• Generate embeddings using a no-code workflow
• Create a simple RAG-style Q&A demo (UI only)