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Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI

ODSC - Open Data Science

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Our goal is to enable all developers to find and fix data issues as effectively as today’s best data scientists.

ML 88
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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

The player tracking data contains the player’s position, direction, acceleration, and more (in x,y coordinates). There are around 3,000 and 4,000 plays from four NFL seasons (2018–2021) for punt and kickoff plays, respectively. The data distribution for punt and kickoff are different.

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Why We Started the Data Intelligence Project

Alation

In 2018, American Family Insurance became an Alation customer and I became the product owner for the AmFam catalog program. To answer these questions we need to look at how data roles within the job market have evolved, and how academic programs have changed to meet new workforce demands. The data scientist.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. He has collaborated with the Amazon Machine Learning Solutions Lab in providing clean data for them to work with as well as providing domain knowledge about the data itself.

ML 100
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Create high-quality datasets with Amazon SageMaker Ground Truth and FiftyOne

AWS Machine Learning Blog

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. To learn more about Ground Truth, refer to Label Data , Amazon SageMaker Data Labeling FAQs , and the AWS Machine Learning Blog.

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Capital One’s data-centric solutions to banking business challenges

Snorkel AI

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance.

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Capital One’s data-centric solutions to banking business challenges

Snorkel AI

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance.