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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. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 278
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Data Insights Assure Quality Data and Confident Decisions!

Smarten

This helps you select the predictors that have the greatest impact, making it easier to create an effective predictive model. Column Metadata – Provides information on the dataset’s recency, such as the last update and publication dates.

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A Few Proven Suggestions for Handling Large Data Sets

Smart Data Collective

Data visualization enables you to: Make sense of the distributional characteristics of variables Easily identify data entry issues Choose suitable variables for data analysis Assess the outcome of predictive models Communicate the results to those interested. It’s a good idea to record metadata.

Metadata 130
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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

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. ” For example, these tools may offer metadata-based notifications. What is Data in Use?

Testing 169
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How to Operationalize Data From Multiple Sources to Deliver Actionable Insights

Speaker: Speakers from SafeGraph, Facteus, AWS Data Exchange, SimilarWeb, and AtScale

Join this webinar to learn how to blend Geospatial data (from SafeGraph), Financial Market and Transaction Data (from Facteus), & Global Websites Visit and Engagement KPIs (from SimilarWeb) to enrich, augment, and improve self-service analytics as well as predictive models.

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How a data fabric overcomes data sprawls to reduce time to insights

IBM Big Data Hub

By using metadata-enriched AI and a semantic knowledge graph for automated data enrichment, a data fabric continuously identifies and connects data from disparate data stores to discover relevant relationships between the available data points. How does a data fabric impact the bottom line?

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task. Our customized profile, complete with key metadata and variable descriptions.