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Enhancing customer care through deep machine learning at Travelers

CIO

Collectively, the scope spans about 1,600 data analytics professionals in the company and we work closely with our technology partnersâ??more that cover areas of software engineering, infrastructure, cybersecurity, and architecture, for instance. But we have to bring in the right talent. more than 3,000 of themâ??that

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Supporting Diverse ML Systems at Netflix

Netflix Tech

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

System 90
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Why Are We Excited About the REAN Cloud Acquisition?

Hu's Place - HitachiVantara

Cloud-based spending will reach 60% of all IT infrastructure and 60-70% of all software, services, and technology spending by 2020. Hybrid clouds must bond together the two clouds through fundamental technology, which will enable the transfer of data and applications.

Cloud 78
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Netflix at AWS re:Invent 2019

Netflix Tech

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Instead, we provide them with delightfully usable ML infrastructure that they can use to manage a project’s lifecycle. Wednesday?—?December

AWS 40
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Netflix at AWS re:Invent 2019

Netflix Tech

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Instead, we provide them with delightfully usable ML infrastructure that they can use to manage a project’s lifecycle. Wednesday?—?December

AWS 40
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Bringing an AI Product to Market

O'Reilly Media - Ideas

Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) For example, an AI product that helps a clothing manufacturer understand which materials to buy will become stale as fashions change. Data Quality and Standardization.

Marketing 145
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Demystifying MLOps: From Notebook to ML Application

Xebia

Data science is generally not operationalized Consider a data flow from a machine or process, all the way to an end-user. 2 In general, the flow of data from machine to the data engineer (1) is well operationalized. You could argue the same about the data engineering step (2) , although this differs per company.