Key Actions to Prevent ML Failure Due to COVID-19-Related Data Drift

Key Actions to Prevent Machine Learning Failure Due to COVID-19-Related Data Drift


Quite a few organizations are experiencing significant decay of their machine learning models due to ongoing shifts in business and consumer behavior during the COVID-19 pandemic.

Data and analytics leaders must understand their options to remedy this and better prepare for the future.

Read this report to discover impactful recommendations to prevent Machine Learning failure in your organization.

At Tangent Works, we are pleased to have collaborated on this report.


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"Deploy technology and best practices, such as ModelOps and MLOps, that can help to continuously monitor, detect and alert ML performance in production."

Gartner Research

"Utilize technology (small data AI, synthetic data, model stacking) to strengthen capabilities to address and overcome issues of ML performance deterioration."

Gartner Research

"Reassess current AI investments and the AI project portfolio, revamping projects that can be accomplished through off-the-shelf solutions and prioritizing ML projects that directly help with process efficiency, productivity increases, building up resilience and contributing to significant cost savings."

Gartner Research

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About Tangent Works

Tangent Works was founded in 2014 by a team of data scientists and mathematicians who believed in the power of predictive modeling for optimizing operations and reducing costs, but found that the existing methods were far too complex for broad adoption. That why they developed the Tangent Information Modeler TIM.

TIM’s mission is to de-mystify predictive modeling and anomaly detection and make them accessible and productive for a broad range of users and use cases.

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