Remove 2016 Remove Interactive Remove Predictive Modeling Remove Risk
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A Window Into the Future of Data in Motion and What It Means for Businesses

CIO Business Intelligence

Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest. Around 2016, we started talking about data in motion within the context of an enterprise data platform.

IoT 92
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A Window Into the Future of Data in Motion and What It Means for Businesses

Cloudera

Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest. Around 2016, we started talking about data in motion within the context of an enterprise data platform.

IoT 97
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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. 2016) for an example of this technique (LIME). PDPs for the bicycle count prediction model (Molnar, 2009).

Modeling 139
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Tackling Bias in Machine Learning

Insight

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more. Overview of what this project aims to build.