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A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

The digitization of internal processes came in 2011, when the company decided to streamline its internal data management, quality control, project management, and communication processes through digital tools and platforms. js and React.js.

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Understanding the different types and kinds of Artificial Intelligence

IBM Big Data Hub

For example, Apple made Siri a feature of its iOS in 2011. Reactive AI stems from statistical math and can analyze vast amounts of data to produce a seemingly intelligence output. This early version of Siri was trained to understand a set of highly specific statements and requests.

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A blazingly fast database in a data-driven world

IBM Big Data Hub

We founded MemSQL (the original name of SingleStore) in 2011. Around 2011, we worked with a hot gaming company with a real-time analytics use case to understand what their users were doing in the moment to optimize the gaming experience by monitoring how users interacted with the game.

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Clustering in R

Domino Data Lab

According to David Madigan, the former chair of Department of Statistics and current Dean of Faculty of Arts and Sciences and Professor of Statistics at Columbia University, a good metric for determining the optimal number of clusters is Hartigan’s rule (J. shows the Gap statistic for a number of different clusters.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression -based models for their business needs. In addition to the model metrics discussed above for classification, DataRobot similarly provides fit metrics for regression models, and helps the modeler visualize the spread of model errors.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

But more dynamic information like freshness, statistics, access controls, owners, documentation, best uses of the data, and lineage also need to be considered to be part of the data product and interface of the data. . Back in 2011, Facebook ran into a problem with building clusters big enough to hold all data. Figure 2.

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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.