NEWS

NVIDIA and VMware partner

Highly scalable, more secure hybrid cloud platform to help enterprises migrate, modernize and deploy next-generation applications.

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NEWS

Spherica launches Synaptek, a new Intelligent Automation division

Synaptek is a new Digital Innovation Division dedicated to enabling better ways of working through intelligent automation.

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NEWS

Mphasis launches AI solutions on the AWS Marketplace

Mphasis has announced the availability of new Deep Learning algorithms on Amazon Web Services (AWS) Marketplace for Machine Learning. The on-demand solutions target practical enterprise use cases such as influence analytics, insurance claims analysis, payment card fraud, and image analytics for supply chain and logistics.

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NEWS

HEMA selects JDA

Leading European retailer selects JDA for a supply chain transformation fueled by Blue Yonder-enabled AI/ML technologies.

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Cortex Labs launches decentralized AI and Machine Learning network

The first globally decentralized deep learning and AI network has been launched.

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University of Oxford spin-out Mind Foundry launches machine learning platform

Platform makes machine learning simple to deploy and augments knowledge and skillsets of business problem owners.

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AI analytics leader enhances scale, develops new capabilities with new deployment.
GigaSpaces' InsightEdge has been selected by Magic Software Enterprises to power their Magic xpi...
Mphasis has announced the availability of new Deep Learning algorithms on Amazon Web Services (AWS)...
Robotics, assembly and manufacturing experts are currently converting industrial production lines...
Calligo launches 'world’s first' managed service to make machine learning accessible to any...
Cloud Technology Solutions (CTS) has merged with application development and machine learning...
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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