Remove 2006 Remove Modeling Remove Optimization Remove Testing
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The mainframe is dying: Long live the mainframe application!

CIO Business Intelligence

Fujitsu remains very much interested in the mainframe market, with a new model still on its roadmap for 2024, and a move under way to “shift its mainframes and UNIX servers to the cloud, gradually enhancing its existing business systems to optimize the experience for its end-users.” years after its launch in June 2006.

Sales 126
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How Nvidia became a trillion-dollar company

CIO Business Intelligence

Along the way, other uses for the parallel-processing capabilities of Nvidia’s graphical processing units (GPUs) emerged, solving problems with a similar matrix arithmetic structure to 3D-graphics modelling. Some of those models are truly gargantuan: OpenAI’s GPT-4 is said to have over 1 trillion parameters.

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Analytics On The Bleeding Edge: Transforming Data's Influence

Occam's Razor

The first component is a gloriously scaled global creative pre-testing program. We pre-test pretty much everything in an online lab ish environment, and predict whether a piece of a TV or Billboard or Radio or YouTube or Facebook creative will be successful. Matched market tests. Creative is the thing you see in the ad.

Analytics 131
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

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Building a Named Entity Recognition model using a BiLSTM-CRF network

Domino Data Lab

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111
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Public cloud vs. private cloud vs. hybrid cloud: What’s the difference?

IBM Big Data Hub

Today, these three cloud architecture models are not mutually exclusive; instead, they work in concert to create a hybrid multicloud—an IT infrastructure model that uses a mix of computing environments (e.g., on-premises, private cloud, public cloud, edge) with public cloud services from more than one provider.

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Position2’s Arena Calibrate helps customers drive marketing efficiency with Amazon QuickSight Embedded

AWS Big Data

Position 2 was established in 2006 in Silicon Valley and has a clientele spanning American Express, Lenovo, Fujitsu, and Thales. After all that, we were still missing out on proactive analysis that identifies trends and uncovers optimization opportunities. We work with clients ranging from VC-funded startups to Fortune 500 firms.