Introduction to Qdrant (Vector Database) Using Python

Learn the basics of Qdrant (Vector Database), Indexing the data, snapshots, Python Client with examples and more !
What you'll 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
Requirements:
Python
Fundamentals of Docker and Docker Compose
Basic Linux commands
Description:
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 onPython ProgrammingLinux CommandsDocker and Docker ComposeSome of the highlights of this course areAll lectures have been designed from the ground up to make the complex topics easy to understandAmple working examples demonstrated in the video lecturesDownloadable Python notebooks for the examples that were used in the coursePrecise and informative video lecturesQuiz at the end of every important video lecturesCovers a wide range of fundamental topics in Qdrant After completing this course, you will be able toInstall and work with Qdrant using PythonManage Collections in QdrantPerform vector search on vectors stored in Qdrant collection Filter the search resultsCreate and manage snapshotsUse Qdrant to build scalable real-world AI appsThis course will be updated periodically and enroll now to get lifelong access to this course!
Who this course is for:
Data Scientists
AI Engineers
Machine Learning Engineers
MLOps Engineers
Data Scientists
Anyone who is motivated to learn and work with a Vector database
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