Using aws_ml/aws_comprehend in Aurora PostgreSQL
Sukhpreet Kaur Bedi is a Database Specialist Solutions Architect with AWS focusing on Amazon RDS/Aurora PostgreSQL engines. She helps customers innovate on the AWS platform by building highly available, scalable, and secure database architectures.
Sundar Raghavan is a Principal Database Specialist Solutions Architect at Amazon Web Services (AWS) and specializes in relational databases. He works with customers across multiple industries who work with RDBMS such as Oracle, PostgreSQL, and migrations from Oracle to PostgreSQL on AWS. Previously, Sundar served as a database and data platform architect at Oracle, Cloudera/Horton Works. He enjoys reading, watching movies, playing chess and being outside when he is not working.
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Amazon Aurora machine learning enables you to add ML-based predictions to applications via the familiar SQL programming language, so you don't need to learn separate tools or have prior machine learning experience. It provides simple, optimized, and secure integration between Aurora and AWS ML services without having to build custom integrations or move data around. In this session, learn more about the process of integrating Aurora with the AWS machine learning service Amazon SageMaker to communicate with a model hosted with the Sagemaker service and Amazon Comprehend to find insights and relationships in text.
- 2022 April 8 10:00 PDT
- 50 min
- Silicon Valley 2022
- AWS Data Day