Introduction to the Course
Get a quick overview of the course, covering the course prerequisites, intended audience, structure, and expected learning outcome.
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Welcome to our comprehensive course on vector databases, where you’ll learn everything from generating embeddings to developing impactful applications.
Vector databases are a crucial component of generative AI-based systems. Generative AI is an advanced form of artificial intelligence capable of generating diverse types of synthetic content, such as text, images, audio, and videos, based on patterns and information from existing data. Its recent popularity arises from the ease of its use and the speed with which it can generate high-quality content. This rise in generative AI highlights the growing importance of vector databases, which are essential for managing and retrieving the vast amounts of data involved in AI-driven content creation. This course is designed to help you grasp the essential aspects of this critical data storage and retrieval technology (embeddings and vector databases), enabling quick content generation.
In this lesson, we will provide an overview of the course, including the course prerequisites, intended audience, and course structure.
What is this course about?
This course is about understanding and applying the concept of embeddings and the use of vector databases to modern data applications, boosting their performance, and adding intelligence.
Embeddings serve as numerical representations of data, capturing relationships and patterns within the dataset. Vector databases, on the other hand, provide the infrastructure necessary for efficiently storing and querying these embeddings. By leveraging vector databases, applications can integrate embeddings into search, recommendation, and similarity detection tasks, enhancing overall functionality and performance.
This course covers all the essential knowledge required to leverage embeddings and vector databases to build efficient and intelligent applications. It includes generating embeddings for different types of datasets, exploring and integrating various open-source vector databases to store embeddings, and building efficient and intelligent applications powered by vector data.
Prerequisites
To take this course, you should have:
Familiarity with basic concepts in data science and machine learning is required.
Knowledge of the Python programming language is required.
Understanding of the PyTorch framework and Python libraries such as NumPy, PIL, Matplotlib, scikit-learn, and pandas is required.
Intended audience
This course is designed for data scientists, machine learning engineers, software developers, and anyone interested in exploring the intersection of data management, machine learning, and large-scale modern application development. Whether you want to enhance your existing skills in managing modern-world application data, design and develop large-scale machine-learning-powered systems, or enter the new field of generative AI, this course offers valuable insights and practical techniques for leveraging vector databases and embeddings in your projects.
Course structure
To help navigate through the course, let’s take a brief look at its structure. We’ll begin by briefly introducing vector databases and embeddings and exploring techniques for finding similarities between embeddings. Then, we’ll move on to generating embeddings for different data types separately and then learn to generate embeddings for multiple data types together. All these concepts will be illustrated through coding examples.
After grasping the concept of embeddings and learning how to generate embeddings, we’ll explore various open-source vector databases. We’ll provide an overview of their design and explore the key features of each database so that the readers can analyze and decide which database should be chosen for a given use case. We’ll learn to integrate a vector database into our project via a coding example. Then, we’ll explore some performance optimization techniques used by vector databases.
Along the course, we’ll build different applications, which include finding jobs matching a given query and image, audio, and video similarity detection systems. Toward the end of the course, we’ll apply our learnings to build a practical application, which includes a music recommendation system. Then, we’ll wrap up the course by summarizing our learnings of this course and discussing the next steps in our journey.
Learning outcome
By the end of this course, you’ll have a comprehensive understanding of vector databases and embeddings, practical skills in generating and manipulating embeddings, and the ability to build efficient applications using open-source vector databases. You’ll also be well-prepared to explore advanced topics in vector databases and apply your knowledge to real-world projects.