Master Google Vertex AI – No-Code & Low-Code Training with Best Practice

This Google Vertex AI course goes beyond theory — it’s built for hands-on learners who want to apply no-code AI, prompt engineering, and Gemini models in real-world scenarios.

Whether you’re a marketer, analyst, or product lead, this is your chance to master low-code AI development without needing a technical background.

Learn by Doing: AI Without Code

Use Vertex AI Studio, AutoML, and Agent Builder with simple drag-and-drop workflows.
Get practical experience in building AI models, chatbots, and content tools using Google Cloud’s no-code interface.

Built for Non-Coders & Innovators

This isn’t just for data scientists — it’s designed for digital marketers, content creators, and business users.
Anyone can apply Generative AI with Gemini using guided, real-life use cases.

Full Course Modules You’ll Access

• Foundations of Google Cloud AI & ML
• Working with AutoML (Tabular, Image, Text, Video)
• Deep dive into Gemini prompt engineering
• Build agents with Vertex AI Agent Builder
• Deploy, monitor & manage models without writing code

Google Cloud Aligned Curriculum

Stay ahead with skills that are in-demand by tech-forward companies.
This course aligns with Google’s own AI training roadmap, helping you stand out in AI-powered roles across industries.

This comprehensive course is designed for individuals seeking to become proficient in Google Vertex AI, focusing on its no-code/low-code capabilities, particularly within the Generative AI space, and leveraging Google’s cutting-edge Gemini models.

Module 1: Foundations of AI & Google Cloud Platform for AI

Introduction to AI & ML

• What is AI and ML? (High-level overview)
• Types of ML: Supervised, Unsupervised, Reinforcement Learning (conceptual)
• Key ML terminology: Data, Model, Training, Prediction, Evaluation
• Introduction to Generative AI: What it is and why it’s transformative

Google Cloud Platform (GCP) Essentials for AI

• GCP Account Setup & Project Management
• Identity and Access Management (IAM) for AI resources
• Cloud Storage: Data storage for ML projects
• BigQuery: Data warehousing for large datasets (basic understanding)
• Understanding GCP Regions and Zones

Module 2: Introduction to Google Vertex AI: The Unified AI Platform

Vertex AI Overview

• What is Vertex AI? A comprehensive ML platform.
• Benefits of using Vertex AI: Simplification, scalability, MLOps integration.
• Navigating the Vertex AI Console: Dashboard, key sections (Datasets, Models, Endpoints, etc.).

Data Management in Vertex AI (No-Code/Low-Code)

• Creating and managing Datasets in Vertex AI (Tabular, Image, Text, Video).
• Importing data from Cloud Storage and other sources.
• Introduction to Vertex AI Data Labeling Service (for supervised learning).

Module 3: No-Code Machine Learning with Vertex AI AutoML

Understanding AutoML: Automated ML for Everyone

• How AutoML automates model selection, hyperparameter tuning, and architecture search.
• Scenarios where AutoML is highly effective.

AutoML for Tabular Data

• Building Classification Models (e.g., predicting customer churn).
• Building Regression Models (e.g., predicting sales).
• Dataset preparation and training workflows.

AutoML for Image Data

• Image Classification (e.g., categorizing images).
• Object Detection (e.g., identifying objects within images).
• Dataset preparation (image uploads, annotations).

AutoML for Text Data

• Text Classification (e.g., sentiment analysis).
• Entity Extraction (e.g., extracting key information from text).
• Dataset preparation for text.

AutoML for Video Data

• Video Classification and Action Recognition (brief overview).

Module 4: Generative AI on Vertex AI: Leveraging Gemini & Prompt Engineering

Introduction to Generative AI & Foundational Models

• What are Large Language Models (LLMs) and Multimodal Models?
• Overview of Google’s Foundational Models (PaLM, Imagen, Codey, and Gemini).
• Capabilities of Gemini: Text, Code, Image, Video, Audio.

Vertex AI Studio: Your Generative AI Playground

• Navigating Vertex AI Studio for prompt experimentation and model interaction.
• Model Garden: Discovering and utilizing pre-trained models.

Core Prompt Engineering for Gemini Models (The Art & Science)

• Understanding the Prompt: Components of an effective prompt (Instruction, Context, Input Data, Output Format).
• Prompting Paradigms:
  • Zero-shot Prompting: Getting responses without examples.
  • One-shot Prompting: Providing a single example.
  • Few-shot Prompting: Providing multiple examples to guide the model’s behavior.
  • Chain-of-Thought Prompting (Conceptual): Encouraging step-by-step reasoning.
• Prompt Design Best Practices:
  • Clarity and Specificity: Avoiding ambiguity.
  • Conciseness: Getting to the point.
  • Using Delimiters: Clearly separating parts of the prompt.
  • Specifying Output Format (e.g., JSON, bullet points, Markdown).
  • Iterative Prompt Refinement: Testing and improving prompts.
• Controlling Model Behavior with Parameters:
  • Temperature: Controlling creativity vs. determinism.
  • Top-P, Top-K: Controlling token selection.
  • Max Output Tokens: Limiting response length.
• Safety and Responsible AI in Prompting:
  • Understanding potential biases and harmful outputs.
  • Strategies for mitigating risks through prompt design.

Applying Gemini with Prompt Engineering: Use Cases (No-Code)

• Text Generation:
  • Creative Writing (stories, poems, scripts).
  • Content Creation (blog posts, marketing copy, social media updates).
  • Summarization (long documents, articles).
  • Translation.
• Chatbot Development:
  • Building simple conversational agents for Q&A.
  • Role-playing and persona-based prompting.
• Code Assistance (Gemini Code Assist):
  • Generating code snippets (various languages).
  • Explaining code.
  • Debugging suggestions.
• Image Generation (using Imagen models through Vertex AI Studio):
  • Crafting prompts for desired image outputs.
  • Understanding image editing capabilities (inpainting, outpainting).
• Multimodal Prompting (conceptual):
  • Interacting with Gemini using text and images for combined understanding.

Low-Code Generative AI: Fine-tuning Gemini Models (Conceptual)

• When and why to fine-tune a foundational model.
• The process of preparing data for fine-tuning (focused on providing examples).
• Understanding the benefits of fine-tuning for specific tasks.

Module 5: Deploying, Monitoring & Operationalizing AI (MLOps Light)

Model Evaluation & Explainability

• Interpreting AutoML evaluation metrics.
• Vertex Explainable AI: Understanding model predictions without code.
• Introduction to Responsible AI: Fairness, interpretability, privacy.

Model Deployment

• Deploying AutoML models to Endpoints for online predictions.
• Deploying Generative AI models (built via Vertex AI Studio) for API access.
• Understanding batch predictions.

Model Monitoring (No-Code Aspects)

• Concept of model drift and anomaly detection.
• Setting up basic monitoring for deployed models in Vertex AI.

Introduction to Vertex AI Pipelines (Low-Code Orchestration)

• Understanding the concept of MLOps pipelines for automating ML workflows.
• How Vertex AI Pipelines can orchestrate data, training, and deployment steps. (Focus on its role, not coding pipelines).

Module 6: Building Intelligent Agents with Vertex AI Agent Builder

Introduction to Agent Builder

• What are AI agents and their capabilities?
• How Agent Builder provides a no-code/low-code interface to build agents.

Designing and Configuring Agents

• Defining agent goals and functions.
• Integrating with knowledge bases and tools.
• Leveraging Gemini for natural language understanding and generation within agents.

Deploying and Testing Agents

• Testing agent interactions.
• Simple use cases for agents (e.g., customer service bots, information retrieval).

Learn Generative AI with Gemini, master no-code machine learning, and build intelligent agents — all inside Google’s powerful Vertex AI platform. No programming required.

Built for Non-Coders

Skip the Python. This course is made for marketers, analysts, and curious creators using low-code AI tools inside Google Cloud — fast, visual, and practical.

Hands-On with Gemini Models

Work directly with Gemini-powered Generative AI for content, code, chatbots, and more. Learn prompt engineering that actually works in real use cases.

End-to-End AI Workflow

From data to deployment — get real-world experience with AutoML, Vertex AI Studio, and model monitoring, all without writing code.

Build Your Own AI Agent

Create intelligent support bots and assistants using Vertex AI Agent Builder. Drag, drop, and launch — no dev skills needed.