@awsdevelopers
  @awsdevelopers
AWS Developers | Building ML capabilities with PostgreSQL and pgvector extension-- AWS Fireside Chat @awsdevelopers | Uploaded 1 year ago | Updated 1 hour ago
Generative AI and Large Language Models (LLMs) are powerful technologies for building applications with richer and more personalized user experiences. Application developers who use Amazon Aurora for PostgreSQL or Amazon RDS for PostgreSQL can use pgvector, an open-source extension for PostgreSQL, to harness the power of generative AI and LLMs for driving richer user experiences. Register now to learn more about this powerful technology.

***To learn more about the services featured in this talk, please visit: aws.amazon.com/rds/aurora/?nc2=h_ql_prod_db_aa
To download the slides visit: pages.awscloud.com/rs/112-TZM-766/images/2023_FC-0908-DAT_Slide-Deck.pdf



#AWS
Building ML capabilities with PostgreSQL and pgvector extension  AWS Fireside ChatBuild a UGC Live Streaming App with Amazon IVS: Displaying Stream Credentials (Lesson 3.1)Welcome to AWS Developers!Solving LLM Amnesia: Cross Session MemoryStart building with PL/Rust in Amazon RDS for PostgreSQL - AWS Databases in 15Building a Leaderboard with Amazon Managed Service for Apache Flink | 1/5Choose a Foundation Model For Your Use Case Pt1 - AWS ML Heroes in 15Create, Deploy & Call your Model with GraphQL in Amplify #shortsEnhanced homogeneous migration capabilities with AWS Database Migration Services- AWS Database in 15Integrating Foundation Models into Your Code with Amazon BedrockBuilding distributed data processing workloads with AWS Step FunctionsBuild on Your Terms Using Different Languages and Models #shorts

Building ML capabilities with PostgreSQL and pgvector extension-- AWS Fireside Chat @awsdevelopers

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER