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Data News — must-read 2022 articles

Christophe Blefari

kitsch moment, from me to you ( credits ) Hey you, this is the last article of the year and it's gonna be about the articles and trends that made 2022 according to me. You'll see articles that I've already share during the year. This article is a good way to go to understand how Snowflake works.

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Latest Innovations in Recommendation Systems with LLMs

KDnuggets

In this article, we’ll explore how LLMs are changing recommendation systems as we speak. Let's first start with a brief overview.

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How to Use NumPy to Solve Systems of Nonlinear Equations

KDnuggets

In this article, we’ll explore how to leverage NumPy to solve systems of nonlinear equations, turning complex mathematical challenges into manageable tasks.

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2022 Summer Intern Projects Article #3

DoorDash Engineering

This is the third blog post in a series of articles showcasing our 2022 summer intern projects. If you missed the first or second article the links are here and here. Today these changes are entered as single entry systems that are prone to errors and difficult to reconcile. You can read about each project below.

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Uncertainty Quantification in Artificial Intelligence-based Systems

KDnuggets

The article summarizes the plethora of UQ methods using Bayesian techniques, shows issues and gaps in the literature, suggests further directions, and epitomizes AI-based systems within the Financial Crime domain.

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Kickstart Your 2023 with these 6 Articles – The Meltano Teams Favorite Data Articles of 2022

Meltano

SQL Jinja isn’t enough SQL + Jinja isn’t enough – why we need dataframes by Furcy Pin “In October 2021, Max Beauchemin, creator of Apache Airflow, wrote an excellent article about 12 trends that are currently affecting data engineers. At the end of 2022 we decided to collect the blogs we enjoyed the most over the year.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

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