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The new enterprise data dilemma

Published in AI Big Data/Analytics-as-a-Service Compliance/Security Data Analytics Infrastructure Machine Learning Software

Data analytics and ML in a multi-cloud enterprise. By Vinay Wagh, Director of Product at Databricks.

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Key Trends Framing the State of AI and ML

Published in AI Everything AI Machine Learning

By Rachel Roumeliotis, VP of Data and AI at O’Reilly.

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Deploying Machine Learning Models to Maximise Business Impact

Published in AI Machine Learning

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’

Published in AI Machine Learning

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

Published in AI Machine Learning

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

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Next generation machine learning powered by graph analytics

Published in AI AIOps Machine Learning

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.

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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AI

The new enterprise data dilemma

Key Trends Framing the State of AI and ML

Deploying Machine Learning Models to Maximise Business Impact

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

How Machine Learning just made software testing smarter

Next generation machine learning powered by graph analytics

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