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Garbage In, Garbage Out: The Crucial Role of Data Quality in AI

Unite.AI

AI algorithms learn from data; they identify patterns, make decisions, and generate predictions based on the information they're fed. Consequently, the quality of this training data is paramount. AI's Role in Improving Data Quality While the problem of data quality may seem daunting, there is hope.

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Chuck Ros, SoftServe: Delivering transformative AI solutions responsibly

AI News

“Managing dynamic data quality, testing and detecting for bias and inaccuracies, ensuring high standards of data privacy, and ethical use of AI systems all require human oversight,” he said. Want to learn more about AI and big data from industry leaders?

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Understanding Data Governance

IBM Journey to AI blog

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

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Navigating the AI Gold Rush: Unveiling the Hidden Costs of Technical Debt in Enterprise Ventures

Unite.AI

Technical debt, in the simplest definition, is the accrual of poor quality code during the creation of a piece of software. When it comes to AI, just over 72 % of leaders want to adopt AI to improve employee productivity, yet the top concern around implementing AI is data quality and control. What Is Technical Debt?

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

AWS Machine Learning Blog

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Workflow B corresponds to model quality drift checks.

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McKinsey QuantumBlack on automating data quality remediation with AI

Snorkel AI

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.