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The quest for high-quality data

O'Reilly on Data

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning.

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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Consider deep learning, a specific form of machine learning that resurfaced in 2011/2012 due to record-setting models in speech and computer vision. Machine learning is not only appearing in more products and systems, but as we noted in a previous post , ML will also change how applications themselves get built in the future.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. It is frequently used for risk analysis.

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AI In Analytics: Today and Tomorrow!

Smarten

Data Integration – Businesses can incorporate storage and data security services and infrastructure, thereby making it easier to avoid data silos and to achieve data integration and single-point-of-contact data analytics.

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Trending Technologies for BI & Financial Planning and AnalysisMaking AI Real (Part 2)

Jedox

It quickly processes large amounts of data from internal and external sources, so users can recognize patterns and gain deeper insights to make better decisions. It runs statistics and algorithms (also known as data mining) on masses of historical data to calculate probabilities and future events. advanced analytics.

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Proposals for model vulnerability and security

O'Reilly on Data

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. It seems entirely possible to do the same with customer or transactional data. Watermark attacks. Disparate impact analysis: see section 1.

Modeling 222
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Why you should care about debugging machine learning models

O'Reilly on Data

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model.