Vector Database Fundamentals

Vector databases provide the solid foundation required by large language models to deliver AI-powered similarity searches and recommendation systems for e-commerce recommendations, cybersecurity fraud detection, medical diagnostics, bioinformatics research, and other complex analysis tasks. Begin learning how to take advantage of the efficiencies vector databases offer in this introductory course by IBM. In this introductory microcourse, you'll build your knowledge of vector database fundamentals and explore the importance of vector databases in today’s data management landscape. Then, dive into how vector databases differ from traditional databases and learn about vector database types and their uses. You’ll also gain hands-on experience setting up environments for vector database operations and performing day-to-day database tasks using Chroma DB. You will learn how to: • Set up environments for vector database operations. • Perform update, delete, and collection-related tasks. • Demonstrate vector database skills and implement similarity searches using real-world data sets. This microcourse is built to provide you with broad, foundational vector database knowledge. This course is for engineers, data scientists, machine learning engineers, DevOps engineers, AI Engineers, and others who work with or intend to build large language models (LLMS), generative AI applications, and related transformative technologies. Enroll today and learn how to take advantage of the efficiencies that vector databases offer!

Demonstrate your proficiency with vector database development by completing this capstone course! In this course, you will implement your skills in two real-life inspired projects, a guided practice project, and a final project. You will use your expertise to develop successful solutions. In both projects, you will use ChromaDB to create recommendation systems by generating embeddings for the data in your collection so your users can search the data using their choice of search terms. Based on those search terms and the embeddings, you will perform similarity searches using natural language processing (NLP) algorithms to provide your users with appropriate results. Upon completion, you will have two working ChromaDB vector database projects to showcase to potential employers. Before starting this course, we recommend completing the other mini-courses offered in the specialization.

The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases. Businesses carry out vector searches in NoSQL databases to improve an AI model's search accuracy and efficiency. During this micro course, you'll learn how to store and index vectors in MongoDB, perform vector searches, and apply the techniques in text similarity analysis and building image classification systems. Plus, you'll look at Cassandra, its features for storing and querying vectors, and how to carry out vector searches. You'll also examine how to apply these concepts to building applications for movie recommendation, inventory management, and personalization. Plus, you'll get valuable practice applying your knowledge through hands-on labs and a real-world final project. Note that this micro course is part of the Vector Database Fundamentals specialization, which is ideal for professionals who work with vector databases, relational databases, and NoSQL databases for AI. It requires a basic knowledge of MongoDB, Cassandra, and Node.js.