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Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. with administrative privileges installed on AWS Terraform version 1.5.5 After the key is provisioned, it should be visible on the AWS KMS console.
Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and datalakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.
The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s. The endpoint will be exposed to Salesforce Data Cloud as an API through API Gateway.
Snowflake-managed Iceberg table’s performance is at par with Snowflake native tables while storing the data in public cloud storage. They are Ideal for situations where the data is already stored in datalakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake.
Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. In this post, we will explore building a reusable RAG data pipeline on LangChain —an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless.
With the Amazon Bedrock serverless experience, you can experiment with and evaluate top foundation models (FMs) for your use cases, privately customize them with your data using techniques such as fine-tuning and RAG, and build agents that run tasks using enterprise systems and data sources.
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