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.