Measurement, Predictive Modeling, Statistics and Workshop

Proposals for model vulnerability and security

O'Reilly on Data

MARCH 20, 2019

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

Modeling Machine Learning Predictive Modeling Consulting

Why you should care about debugging machine learning models

O'Reilly on Data

DECEMBER 12, 2019

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.

Machine Learning

Machine Learning Modeling Testing Risk Management

Data Leaders Brief

Proposals for model vulnerability and security

Why you should care about debugging machine learning models

Webinars

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