Harnessing LLMs & Text-Embeddings API with Google Vertex AI

Overview

Unlock the power of Google Cloud’s Vertex AI and take your machine learning projects to the next level with this practical and hands-on course. You’ll explore how to integrate and apply Large Language Models (LLMs) and the Text-Embeddings API to real-world data, enabling smarter search, classification, and summarization applications. By the end of this course, you’ll have built working knowledge of embeddings, vector similarity, and Retrieval-Augmented Generation (RAG) systems. The course begins with environment setup and a primer on API costs, then walks you through deploying and testing text embeddings with Vertex AI. You’ll perform hands-on tasks like generating sentence embeddings and integrating them into your projects using cosine similarity and visualization tools. A deep dive into the Vertex AI Text Embedding API reveals its potential through multimodal embedding concepts, semantic search, and practical use cases. In later modules, you'll transition from theory to powerful applications—building text generators with the Bison model, extracting structured information from unstructured text, and controlling output via temperature and sampling settings. You'll also develop end-to-end solutions like clustering StackOverflow data and implementing ANN search strategies using HNSW versus cosine similarity. This course is designed for data scientists, machine learning engineers, software developers, and cloud practitioners who are interested in building intelligent applications using GenAI. Ideal learners should have a foundational understanding of Python programming, basic knowledge of machine learning, and experience with REST APIs. Familiarity with Google Cloud Platform services and tools is recommended to fully benefit from this intermediate-level course.

Syllabus

Introduction

In this module, we will introduce you to the course, outlining its structure and prerequisites. You will gain a clear understanding of what the course will cover and how the content is organized.

Development Environment Setup & Google Cloud Platform Setup

In this module, we will guide you through setting up the necessary development environment, configuring Google Cloud, and understanding API costs. You will also engage in a hands-on exercise to test sentence embeddings with Vertex AI.

Vertex AI Text Embedding API and Embeddings Crash Course - Deep Dive

In this module, we will dive deep into Vertex AI and its Text Embedding API, exploring both foundational and advanced concepts. The module includes hands-on exercises to help you better understand embeddings, their dimensions, and real-world applications in Generative AI.

Text Generation with Vertex AI Text Embedding API

In this module, we will focus on text generation techniques within Vertex AI, including working with the Bison model. You'll apply hands-on methods for text classification, information extraction, and fine-tuning text output through various sampling techniques.

Hands-on: Application and Real-world Use Cases of Embeddings

In this module, we will apply what you've learned to real-world use cases by building a RAG system and visualizing clusters in StackOverflow data. You’ll also explore techniques to scale embeddings through approximate nearest neighbor search.

Next Steps

In this final module, we will summarize the course content and suggest potential next steps to continue your learning journey in AI and machine learning.