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Vector Databases

from Embeddings to Applications

Sebastian Witalec

Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs.

These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.

At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors.

This course will help you gain the knowledge to make informed decisions about when to apply vector databases to your applications. You’ll explore:

1. How to use vector databases and LLMs to gain deeper insights into your data.

2. Build labs that show how to form embeddings and use several search techniques to find similar embeddings.

3. Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

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What's inside

Syllabus

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Explores the rapidly growing field of vector databases and their applications

Taught by leading expert Sebastian Witalec

Provides a comprehensive overview of vector database concepts and techniques

Covers advanced techniques such as Retrieval Augmented Generation (RAG)

Suitable for intermediate to advanced learners in data science or machine learning

Requires prior knowledge of natural language processing and machine learning

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Reviews summary

Practical guide to vector databases & rag

According to students, this course provides a highly practical and relevant understanding of vector databases and their application, especially for Retrieval Augmented Generation (RAG) with LLMs. Learners consistently highlight the clear explanations of complex topics and the value of the hands-on labs and practical demonstrations. While generally praised for its foundational strength, some note that a prior understanding of machine learning or natural language processing concepts is beneficial to fully leverage the course content.

Covers core concepts thoroughly, not an exhaustive deep dive.

"It provides an excellent overview but doesn't dive deep into specific commercial databases."

"Great introduction, but for advanced topics, I needed further research."

"Wish there were more examples using different vector database providers."

Labs and demos are invaluable for practical skill development.

"The hands-on labs were invaluable for cementing my knowledge."

"Working through the demos made the concepts stick much better."

"I appreciated the practical coding exercises throughout the modules."

Complex concepts are explained with exceptional clarity.

"The explanations were very clear, breaking down complex concepts effectively."

"I finally understood embeddings and similarity search after this course."

"The way it simplifies complex topics is amazing."

Excellent for building real-world RAG applications.

"The course truly helped me build a practical understanding of RAG applications."

"I loved how it connected vector databases directly to LLMs for real use cases."

"Gave me the confidence to apply RAG in my own projects at work."

Benefits from existing ML/NLP background for optimal learning.

"I found it challenging without a solid ML/NLP background; brush up first!"

"Having some prior experience with embeddings helped immensely."

"While clear, some parts move quickly if you're new to the field."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Databases: from Embeddings to Applications with these activities:

Compile a list of resources on vector databases

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Expand your knowledge by compiling a list of resources that provide further insights into vector databases.

Browse courses on Vector Databases

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Conduct online research
Gather information from books and articles
Organize and categorize the resources

Review relevant vector databases concepts

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Refresh your understanding of vector databases concepts to strengthen the foundation for this course.

Browse courses on Vector Databases

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Read through your old course notes
Review online resources

Review Data Structures and Algorithms

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Solidify your understanding of basic data structures and algorithms, ensuring a solid foundation for working with vector databases.

Browse courses on Data Structures

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Review online resources on data structures (e.g., arrays, linked lists, trees).
Try out coding problems that focus on implementing basic data structures.
Review time and space complexity analysis for common algorithms.

Seven other activities

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Practice using vector databases

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Strengthen your skills in using vector databases through practical exercises.

Browse courses on Vector Databases

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Complete the labs provided in the course
Find additional online exercises

Attend Meetups or Conferences on Vector Databases

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Connect with professionals in the field, broaden your network, and stay updated on industry trends and advancements.

Browse courses on Vector Databases

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Identify local or online meetups or conferences related to vector databases.
Attend sessions, engage in discussions, and network with experts.

Explore Vector Database Tutorials and Examples

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Gain practical experience with vector databases by following guided tutorials and examples, reinforcing concepts covered in the course.

Browse courses on Vector Databases

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Find online tutorials on using vector databases like FAISS or Milvus.
Follow along and build projects to apply vector database techniques.

Create a presentation on vector databases

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Demonstrate your understanding by creating a presentation that summarizes the key concepts of vector databases.

Browse courses on Vector Databases

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Gather information
Organize your content
Design your slides
Practice your presentation

Practice Vector Embedding and Search

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Develop proficiency in forming embeddings and searching for similar vectors, honing your skills in working with vector databases.

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Use online platforms or resources (e.g., Hugging Face) for pre-trained embeddings.
Experiment with different embedding techniques (e.g., Word2Vec, GloVe).
Practice using vector search algorithms (e.g., LSH, Locality Sensitive Hashing).

Contribute to a vector database project

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Contribute to a vector database project to enhance your practical skills and gain real-world experience.

Browse courses on Vector Databases

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Find an open-source vector database project
Review the project's documentation
Identify an area to contribute
Submit your contribution

Build a Vector Database Application

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Apply your knowledge to build a vector database-based application, deepening your understanding of real-world use cases.

Browse courses on Image Recognition

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Choose a specific application area (e.g., image search, personalized recommendations).
Design and implement the vector database schema and application logic.
Test and refine your application, evaluating its performance and accuracy.

Career center

Learners who complete Vector Databases: from Embeddings to Applications will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 43 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Vector Databases: from Embeddings to Applications.

Search Engines

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Provides a vision of the future of semantic search. It is written by Bruce Croft, a leading researcher in the field.

Search engines: information retrieval practice...

Paperback

$$$

Speech and Language Processing. An Introduction to...

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This classic textbook covers a wide range of NLP topics, including speech recognition, natural language understanding, and machine translation. It is an excellent resource for students and researchers.

Speech and Language Processing An Introduction to...

Paperback

Deep Learning Recommender Systems

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This recent publication focuses specifically on the application of deep learning and generative AI in recommender systems. It is highly relevant for those interested in contemporary topics and advanced techniques in the field, particularly at the graduate level and for professionals. It includes discussions on industry implementations and architectures.

Vector Databases

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What's inside

Syllabus

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Reviews summary

Practical guide to vector databases & rag

Activities

Career center

Reading list

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