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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

Testing 300
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The quest for high-quality data

O'Reilly on Data

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning. Data programming.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. In retail, they can personalize recommendations and optimize marketing campaigns. Theyre impressive, no doubt.

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Bringing an AI Product to Market

O'Reilly on Data

If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric. CTRs are easy to measure, but if you build a system designed to optimize these kinds of metrics, you might find that the system sacrifices actual usefulness and user satisfaction.

Marketing 364
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What are model governance and model operations?

O'Reilly on Data

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

Modeling 254
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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

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Deep automation: A CIO weapon for turning disruption into opportunity

CIO Business Intelligence

Unlike siloed or shallow automation efforts, deep automation architects a perspective that integrates customer experiences, value streams, human-machine collaboration, and synergistic technologies to create intelligent, self-adjusting businesses. It emphasizes end-to-end integration, intelligent design, and continuous learning.