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How process mining improves IT service management to save your business time and money

IBM Big Data Hub

For example, if a user’s email is down, they submit a ticket to IT. Similarly with ticket reopens: if a ticket is reopened after being resolved, it indicates the need to address an issue with training or IT resources. Process mining is an ideal solution to start planning for automation.

Mining 76
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Is AIIM Membership Worth It? Pros and Cons

AIIM

Groups are created based on geography, professional interests, and even training and certification. For example, here are just a few of my personal favorites: State of the Content Services Industry (research) and How to Be GDPR Compliant (member toolkit). I included this last because it is the least valuable benefit I get from mine.

IT 110
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Catches of the Month: Phishing Scams for September 2023

IT Governance

Corbridge reported the vulnerability to Microsoft, which “validated that the vulnerability is legitimate, but said that it ‘did not meet the bar for immediate servicing’”. You can help educate your staff with IT Governance’s Phishing Staff Awareness Training Programme. It appears, however, in the target inbox as a file, not a link.”

Phishing 110
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Attacks against machine learning — an overview

Elie

which involve feeding training adversarial data to the classifier. The most common attack type we observe is model skewing, where the attacker attempts to pollute training data in such a way that the boundary between what the classifier categorizes as good data, and what the classifier categorizes as bad, shifts in his favor.

Mining 107
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Attacks against machine learning — an overview

Elie

which involve feeding training adversarial data to the classifier. The most common attack type we observe is model skewing, where the attacker attempts to pollute training data in such a way that the boundary between what the classifier categorizes as good data, and what the classifier categorizes as bad, shifts in his favor.

Mining 91
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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. Conduct validation in the error process—This method measures how good the guesswork was by comparing it to known examples when available.