With the growing importance of AI and machine learning in modern applications, data scientists and developers are constantly exploring new and efficient ways to store and analyze large amounts of data. While specialized vector databases like Pinecone exist, they are only a part of the solution and require a model provider like OpenAI to generate the embedding vectors. This often comes with a high price tag and requires data to be stored outside of your own datacenter. Enter PostgresML and pgvector - an approach to using Postgres as a complete vector database solution that can also generate the embedding vectors for AI applications.

In this talk, we will explore the benefits of using Postgres as a vector database for AI, including faster and cheaper processing, increased reliability, and greater accessibility. We will also discuss how PostgresML and pgvector can be leveraged to build AI applications that can analyze large volumes of structured and unstructured data. Additionally, we will highlight the advantages of keeping all your data within your own datacenter, while still benefiting from the capabilities of a specialized vector database.

Whether you are a data scientist or a developer, this talk will provide you with an understanding of how Postgres can be used as a vector database for AI and how it can be integrated into your existing application stack. Join us to learn how PostgresML and pgvector can enhance your AI capabilities, while keeping your data secure and accessible.