Remove 2020 Remove Data Drift Remove Explainability
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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

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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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

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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Continuous AI Adapts to a Changing World

DataRobot Blog

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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Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

AWS Machine Learning Blog

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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Creating An Information Edge With Conversational Access To Data

Topbots

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.