Introduction to Qdrant (Vector Database) Using Python

Introduction to Qdrant (Vector Database) Using Python, available at $54.99, has an average rating of 4.35, with 24 lectures, 11 quizzes, based on 12 reviews, and has 135 subscribers.

You will learn about Basics of Vector databases Introduction to Qdrant and Installing Qdrant Collections, Segments and Points in Qdrant Vector and payload fields in a Collection Vector and Payload indexing Vector similarity search on a Collection and filtering the results based on payload Quantizing the vectors Configuring Qdrant Server This course is ideal for individuals who are Data Scientists or AI Engineers or Machine Learning Engineers or MLOps Engineers or Data Scientists or Anyone who is motivated to learn and work with a Vector database It is particularly useful for Data Scientists or AI Engineers or Machine Learning Engineers or MLOps Engineers or Data Scientists or Anyone who is motivated to learn and work with a Vector database.

Enroll now: Introduction to Qdrant (Vector Database) Using Python

Summary

Title: Introduction to Qdrant (Vector Database) Using Python

Price: $54.99

Average Rating: 4.35

Number of Lectures: 24

Number of Quizzes: 11

Number of Published Lectures: 24

Number of Published Quizzes: 11

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of Vector databases

Introduction to Qdrant and Installing Qdrant

Collections, Segments and Points in Qdrant

Vector and payload fields in a Collection

Vector and Payload indexing

Vector similarity search on a Collection and filtering the results based on payload

Quantizing the vectors

Configuring Qdrant Server

Who Should Attend

Data Scientists

AI Engineers

Machine Learning Engineers

MLOps Engineers

Data Scientists

Anyone who is motivated to learn and work with a Vector database

Target Audiences

Data Scientists

AI Engineers

Machine Learning Engineers

MLOps Engineers

Data Scientists

Anyone who is motivated to learn and work with a Vector database

Qdrant is an Open Source vector database with in-built vector similarity search engine. Qdrant is written in Rust and is proven to be fast and reliable even under high load in production environment. Qdrant provides convenient API to store, search and manage vectors along with the associated payload for the vectors.

This course will provide you with solid practical Skills in Qdrant using its Python interface.  Before you begin, you are required to have basic knowledge on

Python Programming

Linux Commands

Docker and Docker Compose

Some of the highlights of this course are

All lectures have been designed from the ground up to make the complex topics easy to understand

Ample working examples demonstrated in the video lectures

Downloadable Python notebooks for the examples that were used in the course

Precise and informative video lectures

Quiz at the end of every important video lectures

Covers a wide range of fundamental topics in Qdrant

After completing this course, you will be able to

Install and work with Qdrant using Python

Manage Collections in Qdrant

Perform vector search on vectors stored in Qdrant collection

Filter the search results

Create and manage snapshots

Use Qdrant to build scalable real-world AI apps

This course will be updated periodically and enroll now to get lifelong access to this course!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Vector Databases

Lecture 3: Components of a Vector Databases

Lecture 4: Vector Embeddings

Lecture 5: Vector Similarity Metrics

Chapter 2: Qdrant – Basics

Lecture 1: Introduction and Installation

Lecture 2: Qdrant Storage Model

Lecture 3: Collections

Lecture 4: Points

Lecture 5: Loading a Dataset into Qdrant

Lecture 6: Vector Similarity Search in Qdrant – Part 1

Lecture 7: Vector similarity search in Qdrant – Part 2

Chapter 3: Qdrant – Advanced

Lecture 1: Payload Indexes

Lecture 2: Vector Index

Lecture 3: Vector Quantization – Part 1

Lecture 4: Vector Quantization – Part 2

Lecture 5: Snapshots

Lecture 6: Configuring Qdrant

Lecture 7: Optimizers

Lecture 8: Qdrant – Async Python Client

Chapter 4: Qdrant – Examples (Optional)

Lecture 1: Qdrant + Tensorflow

Lecture 2: Qdrant + OpenAI

Lecture 3: Qdrant + LangChain

Chapter 5: Conclusion

Lecture 1: Conclusion

Instructors

Vijay Anand Ramakrishnan
Instructor at Golden Clover Education

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 4 votes

5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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