AWS Machine Learning Blog

FEBRUARY 23, 2023

This post explains the functions based on a modular pipeline approach. For more information about distributed training with SageMaker, refer to the AWS re:Invent 2020 video Fast training and near-linear scaling with DataParallel in Amazon SageMaker and The science behind Amazon SageMaker’s distributed-training engines.

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Snorkel AI

JANUARY 31, 2023

Valuable data, needed to train models, is often spread across the enterprise in documents, contracts, patient files, and email and chat threads and is expensive and arduous to curate and label. Inevitably concept and data drift over time cause degradation in a model’s performance.

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Snorkel AI

JANUARY 31, 2023

Valuable data, needed to train models, is often spread across the enterprise in documents, contracts, patient files, and email and chat threads and is expensive and arduous to curate and label. Inevitably concept and data drift over time cause degradation in a model’s performance.

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Data Drift Explainability Data Scientist AI 52

DataRobot Blog

MARCH 31, 2022

For example, in March 2020, AI-driven supply chain management systems failed to predict the panic buying of toilet paper and antiseptic wipes. The AI systems had not been trained on data that included pandemics. Data Drift assesses how the distribution of data changes across all features. Request a demo.

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Topbots

JUNE 29, 2023

We will focus on the six requirements that seem most important for the task: accuracy, scalability, speed, explainability, privacy and adaptability over time. Adaptability over time To use Text2SQL in a durable way, you need to adapt to data drift, i. the changing distribution of the data to which the model is applied.

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