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Automating Data Quality Checks with Dagster and Great Expectations

Analytics Vidhya

Introduction Ensuring data quality is paramount for businesses relying on data-driven decision-making. As data volumes grow and sources diversify, manual quality checks become increasingly impractical and error-prone.

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Enhanced observability for AWS Trainium and AWS Inferentia with Datadog

AWS Machine Learning Blog

AWS AI chips, Trainium and Inferentia, enable you to build and deploy generative AI models at higher performance and lower cost. The Datadog dashboard offers a detailed view of your AWS AI chip (Trainium or Inferentia) performance, such as the number of instances, availability, and AWS Region.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning Blog

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. Such agents orchestrate interactions between models, data sources, APIs, and applications.

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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning Blog

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Flipboard

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

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A Guide to Continuous Data Quality Monitoring for AWS Athena

Hacker News

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Automating model customization in Amazon Bedrock with AWS Step Functions workflow

AWS Machine Learning Blog

To enable secure and scalable model customization, Amazon Web Services (AWS) announced support for customizing models in Amazon Bedrock at AWS re:Invent 2023. This allows customers to further pre-train selected models using their own proprietary data to tailor model responses to their business context. Git Installed.

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