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The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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Predictive Analytics Supports Citizen Data Scientists!

Smarten

To accomplish these goals, businesses are using predictive modeling and predictive analytics software and solutions to ensure dependable, confident decisions by leveraging data within and outside the walls of the organization and analyzing that data to predict outcomes in the future.

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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 278
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What is Model Risk and Why Does it Matter?

DataRobot Blog

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses.

Risk 111
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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

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How to Set AI Goals

O'Reilly on Data

Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk. There’s a lot of overlap between these factors. Defining them precisely isn’t as important as the fact that you need all three.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.