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An Accurate Approach to Data Imputation

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential. The post An Accurate Approach to Data Imputation appeared first on Analytics Vidhya.

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EU Cookie / Privacy Laws: Implications On Data Collection And Analysis

Occam's Razor

The way data is collected online and what happens to it is a much-scrutinized issue (and rightly so). Digital data collection is also exceedingly complex, perhaps a reflection of the organic nature, and subsequent explosion, of the internet. Web Data Collection Context: Cookies and Tools.

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The unreasonable importance of data preparation

O'Reilly on Data

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.

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Deep automation in machine learning

O'Reilly on Data

have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.

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Avoiding Toxicity in Generative AI

David Menninger's Analyst Perspectives

Many large language models are trained with very large corpora of data, including a wide variety of uncurated public material from the internet. Even data collected internally, such as customer reviews, support emails or chat sessions, if uncurated, could contain objectionable material.

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4 Ways To Grow Your Business With Big Data

Smart Data Collective

Businesses already have a wealth of data but understanding your business will help you identify a data need – what kind of data your business needs to collect and if it collects too much or too little of certain data. Collecting too much data would be overwhelming and too little – inefficient.

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UK Government tests frictionless trade models with Ecosystem of Trust pilots

IBM Big Data Hub

The UK government’s Ecosystem of Trust is a potential future border model for frictionless trade, which the UK government committed to pilot testing from October 2022 to March 2023. The models also reduce private sector customs data collection costs by 40%.

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