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

Flipboard

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. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product.

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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions

ODSC - Open Data Science

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning Blog

Medical data restrictions You can use machine learning (ML) to assist doctors and researchers in diagnosis tasks, thereby speeding up the process. However, the datasets needed to build the ML models and give reliable results are sitting in silos across different healthcare systems and organizations.

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How to Build ETL Data Pipeline in ML

The MLOps Blog

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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Why Your Data Governance Strategy is Failing

Alation

Without a doubt, no company can achieve lasting profitability and sustainable growth with a poorly constructed data governance methodology. Today, all companies must pursue data analytics, Machine Learning & Artificial Intelligence (ML & AI) as an integral part of any standard business plan.

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Ensure Success with Trusted Data When Moving To The Cloud

Precisely

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Insufficient skills, limited budgets, and poor data quality also present significant challenges.

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How to Power Successful AI Projects with Trusted Data

Precisely

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. Building data literacy across your organization empowers teams to make better use of AI tools. The impact? The stakes are very high.

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