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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example). ML security audits. Discrimination remediation.

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Where’s the ROI for AI? CIOs struggle to find it

CIO

“A chisel in the hands of a trained professional can create amazing things; a chisel in the hands of an amateur can be a lost opportunity.” Kane has seen companies roll out Microsoft Copilot, for example, without any employee training about its uses. Close behind were machine learning and natural language processing.

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Machine Learning at scale: first impressions of Kubeflow

OpenCredo

Machine learning has great potential for many businesses, but the path from a Data Scientist creating an amazing algorithm on their laptop, to that code running and adding value in production, can be arduous. Ideally, this would be automatic, so your data scientists aren’t caught up training and retraining the same model.

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The Newest FIFA World Cup Referee: Human-in-the-Loop Machine Learning

Cloudera

C (Cloudera is headquartered in the US, but we also recognize the superiority of the metric system). The data innovation that I was most excited to learn about though is the implementation of a human-in-the-loop (HITL) machine learning (ML) solution to assist referees in more accurately calling offsides.

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Shelf.io closes huge $52.5M Series B after posting 4x ARR growth in the last year

TechCrunch

The company announced an impressive set of metrics this morning, including that from July 2020 to July 2021, it grew its annual recurring revenue (ARR) 4x. Then, after training models and staff, the company’s software can begin to provide support staff with answers to customer questions as they talk to customers in real time.

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Interpretability in Machine Learning: the Look into Explainable AI

Altexsoft

Domingos referenced a common truth about complex machine learning models, where deep learning belongs. Since they are trained rather than directly programmed, you can hardly tell how exactly they arrive at decisions. In this article, we’ll talk about the interpretability of machine learning models.

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Automated Claims Processing: Using RPA and Machine Learning to Manage Insurance Claims

Altexsoft

Customer satisfaction score (CSAT) and Net Promoter Score (NPS) are the most important metrics for any insurance company. But it does need more advanced approaches that mimic human perception and judgment like AI, Machine Learning, and ML-based Robotic Process Automation. Hire machine learning specialists on the team.