Archive
June 2018

In the previous article in this Machine Learning series, we looked at Linear Regression with Apache® Ignite™. Now let’s take the opportunity to try another Machine Learning algorithm. This time we’ll look at k-Nearest Neighbor (k-NN) Classification. This algorithm is useful for determining class membership, where we classify an object based upon the most common class amongst its k nearest…
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Summer begins today but things are not slowing down here on the community front! Last week, on June 13, GridGain’s Rob Meyer moderated a panel discussion around distributed systems and the future of in-memory computing at the Bay Area In-Memory Computing Meetup in Menlo Park, California. Denis Magda, director of product management and Apache Ignite PMC chair, sat on the panel. That's him to…
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The beta release of GridGain Cloud was announced today. It's the only in-memory cache-as-a-service that allows users to rapidly deploy a distributed in-memory cache and access it using ANSI-99 SQL, key-value or REST APIs. Why is this huge? Because it gives users in-memory computing performance in the cloud, which can be massively scaled out and can be deployed in minutes for caching…
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Ravikanth Durgavajhala is an SD solutions architect focusing on Big Data & AI at Intel Corp. He’ll be speaking at the In-Memory Computing Summit Europe in London on June 25 at 11 a.m.  I connected with him recently about his session, “Expansion of System Memory using Intel Memory Drive Technology,” to share some insights in advance for those attending the conference – as well as for those…
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The full roster of keynotes for the second-annual In-Memory Computing Summit Europe, June 25-26 in London, was announced today. GridGain organizes the biannual summit (held in Silicon Valley in autumn and Europe in spring), and this month's successful event will be thanks to the conference committee, the speakers, sponsors -- and of course, none of it would be possible without the hard work of…
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In the previous article in this Machine Learning series, we looked at the Apache® Ignite™ Machine Learning Grid. Now let’s take the opportunity to drill-down further into some of the Machine Learning algorithms that are supported in Apache Ignite and try out some examples using popular datasets. If we search for suitable datasets to use, we can find many that are available. However, one dataset…
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In a previous article, we discussed the Apache® Ignite™ Machine Learning Grid. At that time, a beta release was available. Subsequently, in version 2.4, Machine Learning became Generally Available. Since the 2.4 release, more improvements and developments have been added, including support for Partitioned-Based Datasets and Genetic Algorithms. Many of the Machine Learning examples that are…
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In case you hadn’t noticed, this year’s annual Spark conference is, for the first time, the Spark+AI Summit. The fact that Spark and AI should be together is predictable even without… using AI to figure it out. But there’s only one way to add continuous learning to Spark+AI, to make AI learn and adapt to new information in near real-time like a person. It is not the AllSpark, which is used to…
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