�ݺ�ߣshows by User: wmarkito

�ݺ�ߣshows by User: wmarkito / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: wmarkito / Tue, 25 Oct 2016 05:17:06 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: wmarkito How to Contribute to Apache Geode /slideshow/how-to-contribute-to-apache-geode/67611326 asf-howtocontribute-161025051706
How to Contribute to Apache Geode presenting the git-flow model used by the project, JIRA standards and some URLs for mailing lists and and ASF tools.]]>
How to Contribute to Apache Geode presenting the git-flow model used by the project, JIRA standards and some URLs for mailing lists and and ASF tools.]]> Tue, 25 Oct 2016 05:17:06 GMT /slideshow/how-to-contribute-to-apache-geode/67611326 wmarkito@slideshare.net(wmarkito) How to Contribute to Apache Geode wmarkito How to Contribute to Apache Geode presenting the git-flow model used by the project, JIRA standards and some URLs for mailing lists and and ASF tools. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/asf-howtocontribute-161025051706-thumbnail.jpg?width=120&height=120&fit=bounds" /> How to Contribute to Apache Geode presenting the git-flow model used by the project, JIRA standards and some URLs for mailing lists and and ASF tools.

How to Contribute to Apache Geode from William Markito Oliveira

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0 Implementing a highly scalable stock prediction system with R, Geode, SpringXD and Spark /slideshow/implementing-a-highly-scalable-stock-prediction-system-with-r-geode-springxd-and-spark/52815601 implementingahighlyscalablestockpredictionsystemwithrgeodeandspringxd-final-150915181836-lva1-app6891
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.]]>
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.]]> Tue, 15 Sep 2015 18:18:36 GMT /slideshow/implementing-a-highly-scalable-stock-prediction-system-with-r-geode-springxd-and-spark/52815601 wmarkito@slideshare.net(wmarkito) Implementing a highly scalable stock prediction system with R, Geode, SpringXD and Spark wmarkito Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/implementingahighlyscalablestockpredictionsystemwithrgeodeandspringxd-final-150915181836-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms.

Implementing a highly scalable stock prediction system with R, Geode, SpringXD and Spark from William Markito Oliveira

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0 Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib /slideshow/building-a-stock-prediction-system-with-machine-learning-using-geode-springxd-and-spark-mllib/52208890 qconrio2015-stockpredictionswithspark-geode-zeppelin-150829220835-lva1-app6892
Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib - #QConRio]]>
Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib - #QConRio]]> Sat, 29 Aug 2015 22:08:35 GMT /slideshow/building-a-stock-prediction-system-with-machine-learning-using-geode-springxd-and-spark-mllib/52208890 wmarkito@slideshare.net(wmarkito) Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib wmarkito Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib - #QConRio <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/qconrio2015-stockpredictionswithspark-geode-zeppelin-150829220835-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib - #QConRio

Building a Stock Prediction system with Machine Learning using Geode, SpringXD and Spark MLLib from William Markito Oliveira

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0 Apache Geode (incubating) Introduction with Docker /slideshow/apache-geode-incubating-introduction-with-docker/50073131 meetupdeck-150702004732-lva1-app6892
This is an introduction to Apache Geode (incubating) that explains some of the key concepts of the project, including steps create a build from the source code, how to create a local cluster with and without Docker, and how to create Teeny, a simple but very scalable URL shortener application.]]>
This is an introduction to Apache Geode (incubating) that explains some of the key concepts of the project, including steps create a build from the source code, how to create a local cluster with and without Docker, and how to create Teeny, a simple but very scalable URL shortener application.]]> Thu, 02 Jul 2015 00:47:32 GMT /slideshow/apache-geode-incubating-introduction-with-docker/50073131 wmarkito@slideshare.net(wmarkito) Apache Geode (incubating) Introduction with Docker wmarkito This is an introduction to Apache Geode (incubating) that explains some of the key concepts of the project, including steps create a build from the source code, how to create a local cluster with and without Docker, and how to create Teeny, a simple but very scalable URL shortener application. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/meetupdeck-150702004732-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an introduction to Apache Geode (incubating) that explains some of the key concepts of the project, including steps create a build from the source code, how to create a local cluster with and without Docker, and how to create Teeny, a simple but very scalable URL shortener application.

Apache Geode (incubating) Introduction with Docker from William Markito Oliveira

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0 ApacheCon 2015 - A Stock Prediction System Using OSS /wmarkito/apachecon-2015-a-stock-prediction-system-using-oss-48642879 apachecon2015-stockpredictionsystemusingoss-150527063602-lva1-app6891
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms]]>
Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms]]> Wed, 27 May 2015 06:36:02 GMT /wmarkito/apachecon-2015-a-stock-prediction-system-using-oss-48642879 wmarkito@slideshare.net(wmarkito) ApacheCon 2015 - A Stock Prediction System Using OSS wmarkito Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachecon2015-stockpredictionsystemusingoss-150527063602-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Finance market prediction has always been one of the hottest topics in Data Science and Machine Learning. However, the prediction algorithm is just a small piece of the puzzle. Building a data stream pipeline that is constantly combining the latest price info with high volume historical data is extremely challenging using traditional platforms, requiring a lot of code and thinking about how to scale or move into the cloud. This session is going to walk-through the architecture and implementation details of an application built on top of open-source tools that demonstrate how to easily build a stock prediction solution with no source code - except a few lines of R and the web interface that will consume data through a RESTful endpoint, real-time. The solution leverages in-memory data grid technology for high-speed ingestion, combining streaming of real-time data and distributed processing for stock indicator algorithms

ApacheCon 2015 - A Stock Prediction System Using OSS from William Markito Oliveira

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0 Oracle Service Bus & Coherence Caching Strategies /wmarkito/oracle-service-bus-coherence-caching-strategies osbcoherence-cachingstrategies-130325205148-phpapp02
How to integrate Oracle Coherence and Oracle Service Bus with a in-process and out-of-process caching strategies]]>
How to integrate Oracle Coherence and Oracle Service Bus with a in-process and out-of-process caching strategies]]> Mon, 25 Mar 2013 20:51:48 GMT /wmarkito/oracle-service-bus-coherence-caching-strategies wmarkito@slideshare.net(wmarkito) Oracle Service Bus & Coherence Caching Strategies wmarkito How to integrate Oracle Coherence and Oracle Service Bus with a in-process and out-of-process caching strategies <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/osbcoherence-cachingstrategies-130325205148-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> How to integrate Oracle Coherence and Oracle Service Bus with a in-process and out-of-process caching strategies

Oracle Service Bus & Coherence Caching Strategies from William Markito Oliveira

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0 https://cdn.slidesharecdn.com/profile-photo-wmarkito-48x48.jpg?cb=1689126689 wmarkito.wordpress.com https://cdn.slidesharecdn.com/ss_thumbnails/asf-howtocontribute-161025051706-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-to-contribute-to-apache-geode/67611326 How to Contribute to A... https://cdn.slidesharecdn.com/ss_thumbnails/implementingahighlyscalablestockpredictionsystemwithrgeodeandspringxd-final-150915181836-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/implementing-a-highly-scalable-stock-prediction-system-with-r-geode-springxd-and-spark/52815601 Implementing a highly ... https://cdn.slidesharecdn.com/ss_thumbnails/qconrio2015-stockpredictionswithspark-geode-zeppelin-150829220835-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/building-a-stock-prediction-system-with-machine-learning-using-geode-springxd-and-spark-mllib/52208890 Building a Stock Predi...