Remove Deep Learning Remove Metadata Remove Predictive Modeling Remove Risk
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The most valuable AI use cases for business

IBM Big Data Hub

The IBM team is even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. they can train their own surrogate model.

Modeling 222
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The Cloud Connection: How Governance Supports Security

Alation

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning.