Sunday, 12th July 2020
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Deploying Machine Learning Models to Maximise Business Impact

Building ML Models may be ‘Data Fun’ but using them to support the business is where the value lies. By John Spooner, Head of Artificial Intelligence, EMEA, H2O.ai.

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How to cultivate a data-driven culture with the help of machine learning and ‘smart search’

These days, data is viewed as the lifeblood of organisations. Gartner has been heavily focused on the importance of developing a data-driven culture in the past year, stating that: “Leaders need to cultivate an organisational culture that is data-literate and that values information as an asset.” By Matt Middleton-Leal, EMEA & APAC General Manager at Netwrix.

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How Machine Learning just made software testing smarter

By Eran Kinsbruner, Chief Evangelist, Perfecto (by Perforce).

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Next generation machine learning powered by graph analytics

Machine learning is computationally demanding, not just in terms of processing power but also the underlying graph query language and architecture of the system. We look at how these challenges can be addressed with native graph databases. By Richard Henderson, Solution Architect, TigerGraph EMEA.

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AI and machine learning - data centres need to differentiate themselves to survive

The interest in adding Artificial Intelligence (AI) and Machine Learning (ML) to business models is fast gaining momentum as organisations look to find patterns within their data that can deliver greater business and customer intelligence, and predict future trends. As Gartner highlights, the number of enterprises implementing AI tripled in the past year. However, with Gartner also claiming that more than 30% of data centres that don’t deploy AI and machine learning won’t be operationally and ec...

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Mastering the misconceptions of AI

This article will outline how to master the most common misconceptions surrounding AI and automation so your business can start reaping the many benefits of Robotic Process Automation (RPA). By Alice Henebury, Head of Marketing, Engage Hub.

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Latest Video

LinkedIn Automates All of the Easy Things, and Makes all of the Hard Things Easy

Hear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.

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