Mastering Vector Databases for AI Applications | Arabic
Learn Vector Databases (pinecone & qdrant) in detail for image search and semantic search engines, End-to-End projects.
Ratings 5.00 / 5.00
What You Will Learn!
- What is vector databases, and why we use them?
- Review on embeddings for text data and feature extraction for images
- Mastering pinecone vector database via end-to-end projects
- Semantic Search using pinecone vector database and Deployment
- Image Search using pinecone vector database and Deployment
- Mastering qdrant vector database via end-to-end projects
- Semantic Search using qdrant vector database and Deployment
- Image Search using qdrant vector database and Deployment
- Libraries for image search (classical approach)
- Libraries for semantic search (classical approach)
Description
Welcome to this course
This course is Mastering Vector Databases for AI Applications in Arabic by Eng/Mohammed Agoor.
In this course, we are going on a journey to discover vector databases like (pinecone, Qdrant) and how to get the best benefit from them via end-to-end projects for building semantic search engines and image search engines from scratch up to deployment and testing.
What we learn here is a lot, We will start by discussing the vector databases and their benefits, review embeddings for text data and feature extraction for images, then we will dive into the pinecone vector database and use it for building an end-to-end semantic search engine and an end-to-end image search engine. Then we will dive into another great vector database (Qdrant) and use it also for the same projects.
Are you ready to take your AI skills to the next level? This course is designed to equip you with the knowledge and hands-on experience needed to build semantic search engines and image search engines from scratch to deployment and testing.
What You'll Learn:
Through a series of engaging modules, you'll explore a wealth of concepts and practical techniques:
Vector databases, and their benefits
Review embeddings for text data and feature extraction for images
Pinecone vector database and its cloud service
Pinecone pricing estimator, and (project, index, collection, pod size, pod type) definitions
Connecting pinecone
Semantic search project using pinecone
Pinecone functions (upsert, delete, update, fetch, query)
Deployment of semantic search with pinecone project using FastAPI
Endpoint to search for semantic search with pinecone project
Endpoint to update (index, delete) for semantic search with pinecone project
Image search project using pinecone
Deployment of image search with pinecone project using FastAPI
Endpoint to search for image search with pinecone project
Endpoint to update (index, delete) for image search with pinecone project
Qdrant vector database, (cluster, collection) definitions
Qdrant dashboard
Connecting qdrant
Semantic search project using qdrant
Deployment of semantic search with qdrant project using FastAPI
Endpoint to search for semantic search with qdrant project
Endpoint to update (index, delete) for semantic search with qdrant project
Image search project using qdrant
Deployment of image search with qdrant project using FastAPI
Endpoint to search for image search with qdrant project
Endpoint to update (index, delete) for semantic search with qdrant project
Whether you're an AI enthusiast, developer, or data scientist, this course will empower you with the knowledge and practical skills necessary to excel in utilizing vector databases for AI applications.
Join us now and embark on an enriching learning journey that will set you on the path to mastering vector databases for cutting-edge AI projects.
Enroll today!
Who Should Attend!
- Artificial Intelligence Engineers
- Generative AI Engineers
- Data Scientists/ Analysts
- Students or statisticians
- Anyone with interest in AI field