Category: Data Science

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New Google Cloud Feature Simplifies Data Science Deployments

Source:-cdotrends.com Google Cloud this week announced a new feature that can make it significantly easier for system administrators and data scientists to set up and maintain their specialized data infrastructure environments in the cloud. Called “machine images”, the new feature essentially stores all the information needed to restore a virtual machine. While this can already be done by an older feature known as “custom images”, machine images can span multiple disks and contains instance properties of individual machines, instance metadata,

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Data Science, Data Storage Leave a comment

The three keys to a successful, data-driven 2020

Source:-gigabitmagazine.com It won’t come as a surprise to anyone that data growth is on the up, but what may be less widely known is that the places where data is being generated are starting to change rapidly. Businesses will start to see how more of their data is being produced in the cloud or the edge, rather than in traditional data centres. The main impact of this will be on how these businesses analyse and understand their data in these

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Solving the Data Explosion with Fog and Edge Computing

Source:-cdotrends.com As the number of IoT devices continues to increase – a predicted 75 billion by 2025 to be exact – so do data requirements. In fact, it’s estimated that IoT will generate more than 500 zettabytes of data per year by the end of 2019. To create an environment where IoT devices and applications are seamlessly connected to one another, and their end-users, sufficient computational and storage resources are needed to perform advanced analytics and machine learning, which the

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Predictions 2020: Ushering in 2020 Data Predictions

Source: DevOps.com 2019 brought us more data organizations running more advanced analytics, AI and ML workloads than ever before. 2020 is the year where we’ll see a spike in both the number of technologies and data teams that support these types of workloads internally. We’ll see AI and analytics teams merge into one as the new foundation of the data organization, focused on areas such as moving to the cloud while maintaining on-prem Hadoop, “Kubernetifying” the analytics stack and Hadoop

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Michael Berthold on End-to-End Data Science Using KNIME Analytics Platform

Source:-infoq.com Open source data analytics platform KNIME CEO and co-founder Michael Berthold gave the keynote presentation at this year’s KNIME Fall Summit 2019 Conference. He spoke about the end-to-end data science cycle which mainly includes Create and Productionize categories. The Create category includes “Gather & Wrangle” and “Model & Visualize” phases, whereas the Productionize category consists of “Deploy and Consume” & “Optimize” phases. The Gather & Wrangle phase, when using KNIME software, supports several connectors & transformation nodes and big

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Data Science, IT trends Leave a comment

Four Big Factors Shaping the Future of Data Science

Source:- insidebigdata.com In this special guest feature, Ryohei Fujimaki, Ph.D., Founder and CEO of dotData, discusses how AI and ML are having a profound impact on enterprise digital transformation becoming crucial as a competitive advantage and even for survival. As the field grows, four trends emerge, shaping data science in the next five years. dotData is a spin-off of NEC Corporation and the first company focused on delivering full-cycle data science automation for the enterprise. Dr. Fujimaki is a world-renowned data

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