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Intelligent Cloud-to-Edge Solution with Google Cloud

Data gravity and privacy concerns require federated solutions across public clouds and multiple edge locations. For example, retail stores embed cameras and sensors to track customer purchases, monitor inventory and provide real-time recommendations, but face challenges as forwarding massive volumes of video and sensor data to the cloud for processing is not practical and adds...

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Will Kubernetes Sink the Hadoop Ship?

The popularity of Kubernetes is exploding. IBM is acquiring RedHat for its commercial Kubernetes version (OpenShift) and VMware just announced that it is purchasing Heptio, a company founded by Kubernetes originators. This is a clear indication that companies are increasingly betting on Kubernetes as their multi-cloud clustering and orchestration technology. At the same time, in...

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Big Data Must Begin with a Clean Slate

More than a decade has passed since we coined the term “big data,” and a decade in the tech world is almost infinity. Is big data now obsolete? The short answer is that although big data in itself may still have its place for some apps, the focus has shifted to integrating data-driven insights into...

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In 2018, Can Cloud, Big Data and AI Stand More Turmoil?

The amount of new technologies in 2017 has been overwhelming: The cloud was adopted faster than analysts projected and brought several new tools with it; AI was introduced into just about all areas of our lives; IoT and edge computing emerged; and a slew of cloud-native technologies came into fruition, such as Kubernetes, serverless, and...

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Tutorial: Faster AI Development with Serverless

https://hackernoon.com/tutorial-faster-ai-development-with-serverless-684f3701b004 The two most trending technologies are AI and serverless and guess what? They even go well together. Before getting into some cool examples, let’s start with some AI basics: AI involves a learning phase in which we observe patterns in historical datasets, identify or learn patterns through training and build machine learned models. Once...

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