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Proposals for model vulnerability and security

O'Reilly on Data

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. Applying data integrity constraints on live, incoming data streams could have the same benefits.

Modeling 275
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How Data Integration and Machine Learning Improve Retention Marketing

Business Over Broadway

The bottom line is that you are not able to make the best prediction about your customers because you don’t have all the necessary information about them. Data Integration as your Customer Genome Project. Data Integration is an exercise in creating your customer genome. segmentation on steroids).

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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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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes.

Testing 169
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Why you should care about debugging machine learning models

O'Reilly on Data

If a model is going to be used on all kinds of people, it’s best to ensure the training data has a representative distribution of all kinds of people as well. Interpretable ML models and explainable ML. The debugging techniques we propose should work on almost any kind of ML-based predictive model.

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How Can BI Consulting Services Help Foster Data-driven Decisions

BizAcuity

This strategic approach enables organizations to prioritize data projects that support their key goals, whether they aim to improve customer experience, reduce costs, or expand into new markets. By aligning the data strategy with business needs, companies can focus their resources on initiatives that yield the most value.