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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 219
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Why you should care about debugging machine learning models

O'Reilly on Data

Residual analysis is another well-known family of model debugging techniques. Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. Interpretable ML models and explainable ML. Residual analysis.

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How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

But these measures alone may not be sufficient to protect proprietary information. Even when backed by robust security measures, an external AI service is a tempting, outsized target for potential security breaches: each integration point, data transfer, or externally exposed API becomes a target for malicious actors.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Skater uses different techniques depending on the type of the model (e.g. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. PDPs for the bicycle count prediction model (Molnar, 2009). Creating a PDP for our model is fairly straightforward.

Modeling 139
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How to Take Your Data Visualization Skills to the Next Level

Depict Data Studio

You’ll also learn about the finer points of decluttering that I never have time to cover during workshops, like decluttering visuals for scientific journals. . Isaac has over 20 years of performance management, evaluation, and outcome measurement experience. You’ll learn how to read your existing style guide.