�ݺ�ߣshows by User: alpinedatalabs

�ݺ�ߣshows by User: alpinedatalabs / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: alpinedatalabs / Thu, 18 Sep 2014 19:07:04 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: alpinedatalabs Integrating R and the JVM Platform - Alpine Data Labs' R Execute Operator /slideshow/sf-scala-sep-10-final/39267618 sfscalasep10final-140918190704-phpapp02
Reactive programming is a phenomenal idea, but it's not always achievable "all the way down" in practice. In the real world, one rarely writes entire platforms from scratch and even then, one often needs to integrate with third-party applications that are blocking, stateful, and seem to violate nearly every reactive principle. In my talk, I will explain how Akka is still ideally suited to handle the integration of such systems into both reactive and non-reactive JVM code. To illustrate the above claims, I will talk about Alpine Data Labs' JVM-R integration. Calls to the R language runtime to perform a data science computation are blocking given the constraints of R itself. Sessions have to be maintained since many messages have to be sent per R session (populating the R heap with DTOs, sending the script to be executed, etc.), and each actor can hold a TCP connection to a single R runtime. R is very prone to failure, be it due to poor memory management, dynamically typed, buggy user code, segmentation faults in native R packages, etc. I will show how Akka can handle all of these problems in a graceful manner to help integrate a faulty, non-engineering grade technology like R into a JVM enterprise application. ]]>
Reactive programming is a phenomenal idea, but it's not always achievable "all the way down" in practice. In the real world, one rarely writes entire platforms from scratch and even then, one often needs to integrate with third-party applications that are blocking, stateful, and seem to violate nearly every reactive principle. In my talk, I will explain how Akka is still ideally suited to handle the integration of such systems into both reactive and non-reactive JVM code. To illustrate the above claims, I will talk about Alpine Data Labs' JVM-R integration. Calls to the R language runtime to perform a data science computation are blocking given the constraints of R itself. Sessions have to be maintained since many messages have to be sent per R session (populating the R heap with DTOs, sending the script to be executed, etc.), and each actor can hold a TCP connection to a single R runtime. R is very prone to failure, be it due to poor memory management, dynamically typed, buggy user code, segmentation faults in native R packages, etc. I will show how Akka can handle all of these problems in a graceful manner to help integrate a faulty, non-engineering grade technology like R into a JVM enterprise application. ]]> Thu, 18 Sep 2014 19:07:04 GMT /slideshow/sf-scala-sep-10-final/39267618 alpinedatalabs@slideshare.net(alpinedatalabs) Integrating R and the JVM Platform - Alpine Data Labs' R Execute Operator alpinedatalabs Reactive programming is a phenomenal idea, but it's not always achievable "all the way down" in practice. In the real world, one rarely writes entire platforms from scratch and even then, one often needs to integrate with third-party applications that are blocking, stateful, and seem to violate nearly every reactive principle. In my talk, I will explain how Akka is still ideally suited to handle the integration of such systems into both reactive and non-reactive JVM code. To illustrate the above claims, I will talk about Alpine Data Labs' JVM-R integration. Calls to the R language runtime to perform a data science computation are blocking given the constraints of R itself. Sessions have to be maintained since many messages have to be sent per R session (populating the R heap with DTOs, sending the script to be executed, etc.), and each actor can hold a TCP connection to a single R runtime. R is very prone to failure, be it due to poor memory management, dynamically typed, buggy user code, segmentation faults in native R packages, etc. I will show how Akka can handle all of these problems in a graceful manner to help integrate a faulty, non-engineering grade technology like R into a JVM enterprise application. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sfscalasep10final-140918190704-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reactive programming is a phenomenal idea, but it's not always achievable "all the way down" in practice. In the real world, one rarely writes entire platforms from scratch and even then, one often needs to integrate with third-party applications that are blocking, stateful, and seem to violate nearly every reactive principle. In my talk, I will explain how Akka is still ideally suited to handle the integration of such systems into both reactive and non-reactive JVM code. To illustrate the above claims, I will talk about Alpine Data Labs' JVM-R integration. Calls to the R language runtime to perform a data science computation are blocking given the constraints of R itself. Sessions have to be maintained since many messages have to be sent per R session (populating the R heap with DTOs, sending the script to be executed, etc.), and each actor can hold a TCP connection to a single R runtime. R is very prone to failure, be it due to poor memory management, dynamically typed, buggy user code, segmentation faults in native R packages, etc. I will show how Akka can handle all of these problems in a graceful manner to help integrate a faulty, non-engineering grade technology like R into a JVM enterprise application.

Integrating R and the JVM Platform - Alpine Data Labs' R Execute Operator from alpinedatalabs

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1 Alpine innovation final v1.0 /slideshow/alpine-innovation-final-v10/38032797 alpineinnovationfinalv1-140815134227-phpapp02
Alpine is constantly innovating, ever since the founding of the company based on in-database analytics that went far beyond traditional, in-memory, code-based desktop applications. This initial innovation built on the work of the MADlib team at Greenplum/Pivotal, ultimately inspired by the work of Joe Hellerstein’s team at UC Berkeley. The team then made all of this functionality available in a simple web interface, which enabled enterprise collaboration and a team-based approach to analytics. Later on, Alpine released its first support for Hadoop, enabling complex analytics on Hadoop without any coding, taking care of all the complexity of MapReduce and Hadoop configuration. Most recently, Alpine has been building new capabilities on top of Spark, to offer Hadoop users a new level of performance and scale. ]]>
Alpine is constantly innovating, ever since the founding of the company based on in-database analytics that went far beyond traditional, in-memory, code-based desktop applications. This initial innovation built on the work of the MADlib team at Greenplum/Pivotal, ultimately inspired by the work of Joe Hellerstein’s team at UC Berkeley. The team then made all of this functionality available in a simple web interface, which enabled enterprise collaboration and a team-based approach to analytics. Later on, Alpine released its first support for Hadoop, enabling complex analytics on Hadoop without any coding, taking care of all the complexity of MapReduce and Hadoop configuration. Most recently, Alpine has been building new capabilities on top of Spark, to offer Hadoop users a new level of performance and scale. ]]> Fri, 15 Aug 2014 13:42:27 GMT /slideshow/alpine-innovation-final-v10/38032797 alpinedatalabs@slideshare.net(alpinedatalabs) Alpine innovation final v1.0 alpinedatalabs Alpine is constantly innovating, ever since the founding of the company based on in-database analytics that went far beyond traditional, in-memory, code-based desktop applications. This initial innovation built on the work of the MADlib team at Greenplum/Pivotal, ultimately inspired by the work of Joe Hellerstein’s team at UC Berkeley. The team then made all of this functionality available in a simple web interface, which enabled enterprise collaboration and a team-based approach to analytics. Later on, Alpine released its first support for Hadoop, enabling complex analytics on Hadoop without any coding, taking care of all the complexity of MapReduce and Hadoop configuration. Most recently, Alpine has been building new capabilities on top of Spark, to offer Hadoop users a new level of performance and scale. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/alpineinnovationfinalv1-140815134227-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Alpine is constantly innovating, ever since the founding of the company based on in-database analytics that went far beyond traditional, in-memory, code-based desktop applications. This initial innovation built on the work of the MADlib team at Greenplum/Pivotal, ultimately inspired by the work of Joe Hellerstein’s team at UC Berkeley. The team then made all of this functionality available in a simple web interface, which enabled enterprise collaboration and a team-based approach to analytics. Later on, Alpine released its first support for Hadoop, enabling complex analytics on Hadoop without any coding, taking care of all the complexity of MapReduce and Hadoop configuration. Most recently, Alpine has been building new capabilities on top of Spark, to offer Hadoop users a new level of performance and scale.

Alpine innovation final v1.0 from alpinedatalabs

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1 Predictive analytics from a to z /slideshow/predictive-analytics-from-a-to-z/37508020 predictiveanalyticsfromatoz-140730130731-phpapp02
This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions. On this webinar you’ll come away with the following knowledge: Quickly start your very own Alpine Chorus 4.0 advanced analytics project and export to PMML with ease. Leverage the power of PMML in a simple Fraud Detection example. Operationalize your project with Zementis deployment solutions. ]]>
This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions. On this webinar you’ll come away with the following knowledge: Quickly start your very own Alpine Chorus 4.0 advanced analytics project and export to PMML with ease. Leverage the power of PMML in a simple Fraud Detection example. Operationalize your project with Zementis deployment solutions. ]]> Wed, 30 Jul 2014 13:07:31 GMT /slideshow/predictive-analytics-from-a-to-z/37508020 alpinedatalabs@slideshare.net(alpinedatalabs) Predictive analytics from a to z alpinedatalabs This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions. On this webinar you’ll come away with the following knowledge: Quickly start your very own Alpine Chorus 4.0 advanced analytics project and export to PMML with ease. Leverage the power of PMML in a simple Fraud Detection example. Operationalize your project with Zementis deployment solutions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/predictiveanalyticsfromatoz-140730130731-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions. On this webinar you’ll come away with the following knowledge: Quickly start your very own Alpine Chorus 4.0 advanced analytics project and export to PMML with ease. Leverage the power of PMML in a simple Fraud Detection example. Operationalize your project with Zementis deployment solutions.

Predictive analytics from a to z from alpinedatalabs

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1 Alpine Spark Implementation - Technical /slideshow/alpine-spark-implementation-technical/34300147 2014-05-01mlor-140505125343-phpapp01
Alpine Data Labs presents a deep dive into our implementation of Multinomial Logistic Regression with Apache Spark. Machine Learning Engineer DB Tsai takes us through the technical implementation details step by step. First, he explains how the state of the art Machine Learning on Hadoop is not doing fulfilling the promise of Big Data. Next, he explains how Spark is a perfect match for machine learning through their in-memory cache-ing capability demonstrating 100x performance improvement. Third, he takes us through each aspect of a multinomial logistic regression and how this is developed with Spark APIs. Fourth, he demonstrates an extension of MLOR and training parameters. Finally, he benchmarks MLOR with 11M rows, 123 features, 11% non-zero elements with a 5 node Hadoop cluster. Finally, he shows Alpine's unique visual environment with Spark and verifies the performance with the job tracker. In conclusion, Alpine supports the state of the art Cloudera and Pivotal Hadoop clusters and performances at a level that far exceeds its next nearest competitor.]]>
Alpine Data Labs presents a deep dive into our implementation of Multinomial Logistic Regression with Apache Spark. Machine Learning Engineer DB Tsai takes us through the technical implementation details step by step. First, he explains how the state of the art Machine Learning on Hadoop is not doing fulfilling the promise of Big Data. Next, he explains how Spark is a perfect match for machine learning through their in-memory cache-ing capability demonstrating 100x performance improvement. Third, he takes us through each aspect of a multinomial logistic regression and how this is developed with Spark APIs. Fourth, he demonstrates an extension of MLOR and training parameters. Finally, he benchmarks MLOR with 11M rows, 123 features, 11% non-zero elements with a 5 node Hadoop cluster. Finally, he shows Alpine's unique visual environment with Spark and verifies the performance with the job tracker. In conclusion, Alpine supports the state of the art Cloudera and Pivotal Hadoop clusters and performances at a level that far exceeds its next nearest competitor.]]> Mon, 05 May 2014 12:53:43 GMT /slideshow/alpine-spark-implementation-technical/34300147 alpinedatalabs@slideshare.net(alpinedatalabs) Alpine Spark Implementation - Technical alpinedatalabs Alpine Data Labs presents a deep dive into our implementation of Multinomial Logistic Regression with Apache Spark. Machine Learning Engineer DB Tsai takes us through the technical implementation details step by step. First, he explains how the state of the art Machine Learning on Hadoop is not doing fulfilling the promise of Big Data. Next, he explains how Spark is a perfect match for machine learning through their in-memory cache-ing capability demonstrating 100x performance improvement. Third, he takes us through each aspect of a multinomial logistic regression and how this is developed with Spark APIs. Fourth, he demonstrates an extension of MLOR and training parameters. Finally, he benchmarks MLOR with 11M rows, 123 features, 11% non-zero elements with a 5 node Hadoop cluster. Finally, he shows Alpine's unique visual environment with Spark and verifies the performance with the job tracker. In conclusion, Alpine supports the state of the art Cloudera and Pivotal Hadoop clusters and performances at a level that far exceeds its next nearest competitor. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014-05-01mlor-140505125343-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Alpine Data Labs presents a deep dive into our implementation of Multinomial Logistic Regression with Apache Spark. Machine Learning Engineer DB Tsai takes us through the technical implementation details step by step. First, he explains how the state of the art Machine Learning on Hadoop is not doing fulfilling the promise of Big Data. Next, he explains how Spark is a perfect match for machine learning through their in-memory cache-ing capability demonstrating 100x performance improvement. Third, he takes us through each aspect of a multinomial logistic regression and how this is developed with Spark APIs. Fourth, he demonstrates an extension of MLOR and training parameters. Finally, he benchmarks MLOR with 11M rows, 123 features, 11% non-zero elements with a 5 node Hadoop cluster. Finally, he shows Alpine's unique visual environment with Spark and verifies the performance with the job tracker. In conclusion, Alpine supports the state of the art Cloudera and Pivotal Hadoop clusters and performances at a level that far exceeds its next nearest competitor.

Alpine Spark Implementation - Technical from alpinedatalabs

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1 Don't Gamble With Your Data /slideshow/dont-gamble-with-your-data-32958740/32958740 interopmarch2014slideshare-140331163115-phpapp01
In this engaging presentation, Bruno Aziza covers 3 key Big Data concept: definitions, lessons and best practices. Subjects covered: What is Big Data? Does Hadoop Matter? How to scale Data Science and Data Scientists? For more, go to www.alpinenow.com Access to 30-day trial @ www.start.alpinenow.com ]]>
In this engaging presentation, Bruno Aziza covers 3 key Big Data concept: definitions, lessons and best practices. Subjects covered: What is Big Data? Does Hadoop Matter? How to scale Data Science and Data Scientists? For more, go to www.alpinenow.com Access to 30-day trial @ www.start.alpinenow.com ]]> Mon, 31 Mar 2014 16:31:15 GMT /slideshow/dont-gamble-with-your-data-32958740/32958740 alpinedatalabs@slideshare.net(alpinedatalabs) Don't Gamble With Your Data alpinedatalabs In this engaging presentation, Bruno Aziza covers 3 key Big Data concept: definitions, lessons and best practices. Subjects covered: What is Big Data? Does Hadoop Matter? How to scale Data Science and Data Scientists? For more, go to www.alpinenow.com Access to 30-day trial @ www.start.alpinenow.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/interopmarch2014slideshare-140331163115-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this engaging presentation, Bruno Aziza covers 3 key Big Data concept: definitions, lessons and best practices. Subjects covered: What is Big Data? Does Hadoop Matter? How to scale Data Science and Data Scientists? For more, go to www.alpinenow.com Access to 30-day trial @ www.start.alpinenow.com

Don't Gamble With Your Data from alpinedatalabs

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1 Steven Hillion Presents, "Why Women are Better Data Scientists." /slideshow/steven-hillion-whyarewomenbetterdsthanmen/27787156 stevenhillionwhyarewomenbetterdsthanmen-131031135501-phpapp01
A recent study suggests that in the field of data science (unlike most other fields) women actually get paid more than men. Could this be because they are simply better at doing data science? Steven Hillion presents five reasons – some anecdotal, some based on real data – why women will increasingly lead the way in data science.]]>
A recent study suggests that in the field of data science (unlike most other fields) women actually get paid more than men. Could this be because they are simply better at doing data science? Steven Hillion presents five reasons – some anecdotal, some based on real data – why women will increasingly lead the way in data science.]]> Thu, 31 Oct 2013 13:55:01 GMT /slideshow/steven-hillion-whyarewomenbetterdsthanmen/27787156 alpinedatalabs@slideshare.net(alpinedatalabs) Steven Hillion Presents, "Why Women are Better Data Scientists." alpinedatalabs A recent study suggests that in the field of data science (unlike most other fields) women actually get paid more than men. Could this be because they are simply better at doing data science? Steven Hillion presents five reasons – some anecdotal, some based on real data – why women will increasingly lead the way in data science. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stevenhillionwhyarewomenbetterdsthanmen-131031135501-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A recent study suggests that in the field of data science (unlike most other fields) women actually get paid more than men. Could this be because they are simply better at doing data science? Steven Hillion presents five reasons – some anecdotal, some based on real data – why women will increasingly lead the way in data science.

Steven Hillion Presents, "Why Women are Better Data Scientists." from alpinedatalabs

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1 Strata Big Data Camp 2013 /slideshow/strata-big-data-camp-2013-27786097/27786097 stratabigdatacamp2013-131031131956-phpapp02
Your customers are more brutal than you think. An inspiring talk on how to retain customers and market in the age of analytics. Big Data, Predictive Analysis, and Cloud Applications.]]>
Your customers are more brutal than you think. An inspiring talk on how to retain customers and market in the age of analytics. Big Data, Predictive Analysis, and Cloud Applications.]]> Thu, 31 Oct 2013 13:19:56 GMT /slideshow/strata-big-data-camp-2013-27786097/27786097 alpinedatalabs@slideshare.net(alpinedatalabs) Strata Big Data Camp 2013 alpinedatalabs Your customers are more brutal than you think. An inspiring talk on how to retain customers and market in the age of analytics. Big Data, Predictive Analysis, and Cloud Applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratabigdatacamp2013-131031131956-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Your customers are more brutal than you think. An inspiring talk on how to retain customers and market in the age of analytics. Big Data, Predictive Analysis, and Cloud Applications.

Strata Big Data Camp 2013 from alpinedatalabs

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1 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/sfscalasep10final-140918190704-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sf-scala-sep-10-final/39267618 Integrating R and the ... https://cdn.slidesharecdn.com/ss_thumbnails/alpineinnovationfinalv1-140815134227-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/alpine-innovation-final-v10/38032797 Alpine innovation fina... https://cdn.slidesharecdn.com/ss_thumbnails/predictiveanalyticsfromatoz-140730130731-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predictive-analytics-from-a-to-z/37508020 Predictive analytics f...