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How to establish lineage transparency for your machine learning initiatives

IBM Journey to AI blog

From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes. Have you ever wondered how these algorithms arrive at their conclusions? The answer lies in the data used to train these models and how that data is derived.

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3 Takeaways from Gartner’s 2018 Data and Analytics Summit

DataRobot Blog

Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. 2) Line of business is taking a more active role in data projects.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs.