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Advancements in technologies such as machine learning and Artificial Intelligence (AI) are growing in the workplace – to the benefit of employees who are able to work more efficiently and intelligently than ever before. Dr Davide Zilli, Director of Client Services at Mind Foundry, explains how these tools are helping reduce time-consuming tasks, freeing up human expertise to be used where it is most effective.
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AI experts at CognitionX and Peltarion release new report on where the value in Deep Learning (DL) lies, and where the trend for Deep Learning is moving, based on insight across industries from AI thought-leaders in Europe.
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Databricks has introduced Model Registry, a new capability within MLflow, an open-source platform for the machine learning (ML) lifecycle created by Databricks. The new component enables a comprehensive model management process by providing data scientists and engineers a central repository to track, share, and collaborate on machine learning models. The Model Registry manages the full lifecycle of models and their stage transitions from experimentation to staging and deployment. Since...
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The effects of climate change are seen on an ever-larger scale and governments are increasingly putting policies in place and setting emission targets in an effort to stop temperatures rising. As highlighted by edie, 70% of the global energy demand is currently used by cities and it is predicted that an additional 2.5 billion people will be living in cities by 2050. Looking at these statistics, solutions to decrease the environmental impact of cities while improving the quality of life for...
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Company launches Dropbox Spaces, adds new machine intelligence, collaboration, and admin features.
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Platform helps enterprises fully operationalize AI quickly and is key building block of Infor’s Intelligent CloudSuite.
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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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