Registrations are open for the Vector Databases – From Embeddings to Applications by DeepLearning AI and Weaviate. Free for a limited time only!
About DeepLearning AI
DeepLearning AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. DeepLearning AI has created high-quality AI programs on Coursera that have gained an extensive global following. By providing a platform for education and fostering a tight-knit community, DeepLearning AI has become the pathway for anyone looking to build an AI career.
About Weaviate
Weaviate creates open-source, AI-first infrastructure. They believe that the next wave of software infrastructure is AI-first and that a strong open-source community is a basis for creating high-quality software. For more details click here!
Course Details
Build efficient, practical applications including hybrid and multilingual searches, for diverse industries.
Understand vector databases and use them to develop GenAI applications without needing to train or fine-tune an LLM yourself. Learn to discern when best to apply a vector database to your application.
What you’ll learn in this course?
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:
- How to use vector databases and LLMs to gain deeper insights into your data.
- Build labs that show how to form embeddings and use several search techniques to find similar embeddings.
- Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.
How to Register?
Interested candidates can directly register through this link.
Fee
Free for a limited time only!
