Remove Data Ingestion Remove Data Quality Remove Generative AI
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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT.

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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. Using the services built-in source connectors standardizes and simplifies the work needed to maintain data quality and manage the overall data lifecycle.

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Unlock proprietary data with Snorkel Flow and Amazon SageMaker

Snorkel AI

When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data. What the Snorkel Flow + AWS integrations offer Streamlined data ingestion and management: With Snorkel Flow, organizations can easily access and manage unstructured data stored in Amazon S3.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

The model will be approved by designated data scientists to deploy the model for use in production. For production environments, data ingestion and trigger mechanisms are managed via a primary Airflow orchestration. Workflow B corresponds to model quality drift checks.

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Meet MegaParse: An Open-Source AI Tool for Parsing Various Types of Documents for LLM Ingestion

Marktechpost

As generative AI continues to grow, the need for an efficient, automated solution to transform various data types into an LLM-ready format has become even more apparent. Meet MegaParse : an open-source tool for parsing various types of documents for LLM ingestion. Check out the GitHub Page.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement.

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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

A new data flow is created on the Data Wrangler console. Choose Get data insights to identify potential data quality issues and get recommendations. In the Create analysis pane, provide the following information: For Analysis type , choose Data Quality And Insights Report. For Target column , enter y.