This course provides a hands-on introduction to Google Vertex AI’s Text-Embeddings API, from environment setup to generating and applying embeddings in real-world scenarios. You’ll learn the fundamentals of embeddings, visualize and run similarity searches, and explore applications in Generative AI and LLMs. With guided exercises, you’ll build scalable solutions like RAG systems and apply embeddings for text generation, information extraction, and multimodal tasks. By the end, you’ll be proficient in leveraging Vertex AI for practical AI-driven applications.
Who should take this Course?
The Harnessing Google Vertex AI Online Course is ideal for data scientists, machine learning engineers, and AI developers who want to build, deploy, and scale ML models using Google Cloud’s Vertex AI platform. It is also suitable for students, researchers, and cloud professionals eager to gain hands-on experience with automated machine learning, model monitoring, and end-to-end AI workflows in a production-ready environment.
What you will learn
Configure your environment for efficient Vertex AI usage
Generate embeddings and visualize their relationships effectively
Apply text generation for classification and summarization tasks
Implement cosine similarity and search techniques with embeddings
Build real-world AI solutions using Vertex AI capabilities
Develop a RAG system and analyze StackOverflow data insights
Course Outline
Introduction
Introduction and About the Course - Prerequisites
Course Structure
Development Environment Setup & Google Cloud Platform Setup
Development Environment Setup and API Costs - Overview
Google Cloud Setup
Hands-on: Testing the Vertex AI - Generated a Sentence Embedding
Vertex AI Text Embedding API and Embeddings Crash Course - Deep Dive
Introduction to Vertex AI and Capabilities - Overview
OPTIONAL: Embeddings Crash Course
How are Embeddings Used in GenAI and LLMs and Use Cases
The Embeddings API - Text vs Multimodal Embeddings - Overview
Task Types and Benefits
Multimodal Embeddings Diagram
Hands-on: Embeddings Length - Dimension
Hands-on: Run Cosine Similarity Search on Different Sentences
Hands-on: Visualize Embeddings
Summary
Text Generation with Vertex AI Text Embedding API
TextGenerationModel - Generating Text Using Bison Model
Hands-on: Text Generation - Classification Use Case
Hands-on: Extract Information into Tables and JSON Formats
Hands-on: Controlling Temperature for the Model
Hands-on: TopK and TopP
Hands-on: Transcript Summarization and Extraction
Hands-on: Application and Real-world Use Cases of Embeddings
Cluster Visualization of StackOverflow Question and Answers in 2D
Build Your RAG System with the StackOverflow Data
Scale with the Approximate Nearest Neighbor Search: HNSW vs Cosine Similarity
