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Manage access controls in generative AI-powered search applications using Amazon OpenSearch Service and Amazon Cognito

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A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.

AWS
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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

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Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization. dbt Cloud is a hosted service that helps data teams productionize dbt deployments. Create dbt models in dbt Cloud.

ETL
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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning Blog

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

AWS
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Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

AWS Machine Learning Blog

In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Complete the following steps: Choose an AWS Region Amazon Q supports (for this post, we use the us-east-1 Region). aligned identity provider (IdP).

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Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

Search solutions in modern big data management must facilitate efficient and accurate search of enterprise data assets that can adapt to the arrival of new assets. The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context.

AWS
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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Expand to generative AI use cases with your existing AWS and Tecton architecture After you’ve developed ML features using the Tecton and AWS architecture, you can extend your ML work to generative AI use cases. You can also find Tecton at AWS re:Invent. This process is shown in the following diagram.

ML
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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.