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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). However, joint optimization is possible by increasing both $x_1$ and $x_2$ at the same time.

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Think inside the box: Container use cases, examples and applications

IBM Big Data Hub

From small startups to large, established businesses, container frameworks have proven exceedingly capable of generating stable workflows with optimized runtimes and continuous delivery. Docker containers were originally built around the Docker Engine in 2013 and run according to an application programming interface (API).

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. You just specify your desired price-performance targets to either optimize for cost or optimize for performance or balanced and serverless does the rest.

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How Wallapop improved performance of analytics workloads with Amazon Redshift Serverless and data sharing

AWS Big Data

Wallapop’s initial data architecture platform Wallapop is a Spanish ecommerce marketplace company focused on second-hand items, founded in 2013. Since its creation in 2013, it has reached more than 40 million downloads and more than 700 million products have been listed. The marketplace can be accessed via mobile app or website.

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Build a RAG data ingestion pipeline for large-scale ML workloads

AWS Big Data

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. With optimized configuration, it aims for high recall for the queries. He entered the big data space in 2013 and continues to explore that area.

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Thermo Fisher transforms its customer experience

CIO Business Intelligence

With its business rapidly growing and customer expectations rising, Thermo Fisher Scientific is turning to machine learning and robotic process automation (RPA) to transform the customer experience. in 2013, Alfa Aesar in 2015, Affymetrix and FEI Co. in 2016, and BD Advanced Bioprocessing in 2018.

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Generative AI headlines are outpacing enterprise adoption

CIO Business Intelligence

Many CIOs have become the de facto generative AI professor and spent ample time developing 101 materials and conducting roadshows to build awareness, explain how generative AI differs from machine learning, and discuss the inherent risks. According to Ronanki, they aspired to replace human doctors with machines in diagnosing cancer.