Presented by:

Neelam Koshiya is principal solutions architect at AWS with specialization in GENAI/ML. With a background in software engineering, she moved organically into an architecture role. Her current focus is to help enterprise customers with their cloud-adoption journeys for strategic business outcomes. Her area of depth is machine learning. She likes to build content/mechanisms to scale to larger audience. She is passionate about innovation and inclusion. In her spare time, she enjoys reading and being outdoors.

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PostgreSQL makes it easier to store and query vector data for AI/ML use cases with the pgvector extension. Learning best practices for vector search will help you deliver a high-performance experience to your customers. In this session, learn how to store data from Amazon Bedrock in an Amazon Aurora PostgreSQL-Compatible Edition database and learn what SQL queries and tuning parameters optimize the performance of your application when working with AI/ML data, vector data types, exact and approximate nearest neighbor search algorithms, and vector-optimized indexing.

Date:
2024 November 7 11:40 PST
Duration:
50 min
Room:
Dev: 422
Conference:
Seattle 2024
Language:
Track:
Dev
Difficulty:
Hard