You can now perform batch AI inference within Amazon OpenSearch Ingestion pipelines to efficiently enrich and ingest large datasets for Amazon OpenSearch Service domains.
Previously, customers used OpenSearch’s AI connectors to Amazon Bedrock, Amazon SageMaker, and 3rd-party services for real-time inference. Inferences generate enrichments such as vector embeddings, predictions, translations, and recommendations to power AI use cases. Real-time inference is ideal for low-latency requirements such as streaming enrichments. Batch inference is ideal for enriching large datasets offline, delivering higher performance and cost efficiency. You can now use the same AI connectors with Amazon OpenSearch Ingestion pipelines as an asynchronous batch inference job to enrich large datasets such as generating and ingesting up to billions of vector embeddings.
This feature is available in all regions that support Amazon OpenSearch Ingestion and 2.17+ domains. Learn more from the documentation.
Categories: marketing:marchitecture/analytics,marketing:marchitecture/artificial-intelligence,marketing:marchitecture/databases,general:products/amazon-opensearch-service
Source: Amazon Web Services
Latest Posts
- MC1465983: Dynamics 365 Contact Center Adds Continuous Scheduling for Overnight Shifts
- MC1465860: Microsoft 365 Apps Rolls Out Current Channel Update for September 2026
- AWS Deadline Cloud now supports sharing job bundles
- Amazon Kinesis Data Streams now supports a dry run feature to validate API requests
