Remove 2014 Remove Data Collection Remove Forecasting Remove Risk
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.

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Optimizing clinical trial site performance: A focus on three AI capabilities

IBM Big Data Hub

This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. AI models can be designed to detect anomalies in real-time site performance data.

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Making smart cities safer with data

Cloudera

While such a city might sound like a utopian dream, it could potentially turn into a dystopian nightmare if we overlook the risks brought about by the rise of smart cities. Reflect back on the recent SingHealth breach in Singapore, in which non-medical personal data of 1.5 billion in 2014. IoT opens doors to threats.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.