Using aws_ml/aws_comprehend in Aurora PostgreSQL
Presented by:
Sukhpreet Bedi
Sukhpreet is a Senior PostgreSQL Specialist Solutions Architect at AWS, where she specializes in Amazon RDS and Aurora PostgreSQL engines. As a trusted technical advisor, she guides organizations in building resilient and secure database architectures on AWS. Her expertise extends to implementing Agentic AI solutions on PostgreSQL, helping customers develop intelligent applications that transform their business capabilities through modern database technologies.
Sundar Raghavan
Sundar Raghavan leads a team of Database Specialist Solutions Architect at Amazon Web Services (AWS). He and his team 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.
- Date:
- 2022 April 8 10:00 PDT
- Duration:
- 50 min
- Room:
- Ballroom
- Conference:
- Silicon Valley 2022
- Language:
- Track:
- AWS Data Day
- Difficulty:
- Medium