Remove Data Governance Remove Data Quality Remove Machine Learning
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. To view this series from the beginning, start with Part 1.

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Difference between modern and traditional data quality - DataScienceCentral.com

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Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders.

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Data catalog

Dataconomy

Data catalogs play a pivotal role in modern data management strategies, acting as comprehensive inventories that enhance an organization’s ability to discover and utilize data assets. By providing a centralized view of metadata, data catalogs facilitate better analytics, data governance, and decision-making processes.

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Data quality

Dataconomy

Data quality is an essential factor in determining how effectively organizations can use their data assets. In an age where data is often touted as the new oil, the cleanliness and reliability of that data have never been more critical. What is data quality? million annually.

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Discovering MLOps – The key to efficient machine learning deployment

Data Science Dojo

Ready to revolutionize the way you deploy machine learning? Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. Data Management: Effective data management is crucial for ML models to work well.

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Discovering ML Ops – The key to efficient machine learning deployment

Data Science Dojo

Ready to revolutionize the way you deploy machine learning? Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. Data Management: Effective data management is crucial for ML models to work well.