Vector Database Projects: AI Recommendation Systems

Overview

The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user preferences and drive engagement using AI recommendation system skills has become an essential business need and a highly sought-after skill using vector databases. In this IBM mini-course, you’ll create two shareable projects that demonstrate your proficiency and readiness to develop AI-powered recommendation systems.  You’ll get step-by-step instructions to create a real-life inspired food ordering recommendation system using Chroma DB and Hugging Face models. For your final project, you’ll use Chroma DB or your choice of PostgreSQL, Cassandra, or MongoDB to create a real-life job search recommendation system. This will demonstrate your ability to generate embeddings and implement similarity searches using Hugging Face natural language processing (NLP) algorithms. Ready to start? Bring your vector, NoSQL, or relational database vector search skills to this course.  If you don't already have these skills, you can attain these skills in other Vector Databases Fundamentals Specialization courses.  Enroll today in this mini-course to advance your AI career!

Syllabus

Introduction to AI Models

In this module, you will learn about Hugging Face, a growing, open-source AI community. You will learn about several of their crowd-sourced NLP models and how to incorporate them into your projects. This module also includes a practice project, a non-graded project that provides you with step-by-step instructions for building a recommendation system similar to the final project.

Final Project

In this module, you will work on the final, graded project independently and will be graded by one of your peers. You will also evaluate one of your peers’ projects using a rubric with objective grading criteria. The project has two parts: in Part 1, you’ll build a job recommendation system that returns the top five results based on a text search; in Part 2, you’ll create a food recommendation system that performs a similar task using a PDF as input. Finally, you will have the opportunity to demonstrate your comprehension of the final project by completing a graded quiz.