�ݺ�ߣshows by User: felixcss

�ݺ�ߣshows by User: felixcss / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: felixcss / Sat, 08 Jul 2017 22:03:40 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: felixcss Scalable Data Science in Python and R on Apache Spark /slideshow/scalable-data-science-in-python-and-r-on-apache-spark/77652668 pydatascalabledatasciencepythonrspark-170708220340
In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R? We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark. Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages. Python R Apache Spark ML DL]]>
In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R? We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark. Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages. Python R Apache Spark ML DL]]> Sat, 08 Jul 2017 22:03:40 GMT /slideshow/scalable-data-science-in-python-and-r-on-apache-spark/77652668 felixcss@slideshare.net(felixcss) Scalable Data Science in Python and R on Apache Spark felixcss In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R? We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark. Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages. Python R Apache Spark ML DL <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pydatascalabledatasciencepythonrspark-170708220340-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the world of Data Science, Python and R are very popular. Apache Spark is a highly scalable data platform. How could a Data Scientist integrate Spark into their existing Data Science toolset? How does Python work with Spark? How could one leverage the rich 10000+ packages on CRAN for R? We will start with PySpark, beginning with a quick walkthrough of data preparation practices and an introduction to Spark MLLib Pipeline Model. We will also discuss how to integrate native Python packages with Spark. Compare to PySpark, SparkR is a new language binding for Apache Spark and it is designed to be familiar to native R users. In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable scalable machine learning on Big Data. In addition to talking about the R interface to the ML Pipeline model, we will explore how SparkR support running user code on large scale data in a distributed manner, and give examples on how that could be used to work with your favorite R packages. Python R Apache Spark ML DL

Scalable Data Science in Python and R on Apache Spark from felixcss

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0 SSR: Structured Streaming for R and Machine Learning /slideshow/ssr-structured-streaming-for-r-and-machine-learning/76857968 ss2017-ssr-170612081419
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2]]>
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2]]> Mon, 12 Jun 2017 08:14:19 GMT /slideshow/ssr-structured-streaming-for-r-and-machine-learning/76857968 felixcss@slideshare.net(felixcss) SSR: Structured Streaming for R and Machine Learning felixcss Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ss2017-ssr-170612081419-thumbnail.jpg?width=120&height=120&fit=bounds" /> Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2

SSR: Structured Streaming for R and Machine Learning from felixcss

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0 Apache: Big Data - Starting with Apache Spark, Best Practices /slideshow/apache-big-data-starting-with-apache-spark-best-practices/76162282 apachecon-startingwithapachesparkbestpractices-170520185427
https://apachebigdata2017.sched.com/event/9zs7/starting-with-apache-spark-best-practices-and-learning-from-the-field-felix-cheung-microsoft]]>
https://apachebigdata2017.sched.com/event/9zs7/starting-with-apache-spark-best-practices-and-learning-from-the-field-felix-cheung-microsoft]]> Sat, 20 May 2017 18:54:27 GMT /slideshow/apache-big-data-starting-with-apache-spark-best-practices/76162282 felixcss@slideshare.net(felixcss) Apache: Big Data - Starting with Apache Spark, Best Practices felixcss https://apachebigdata2017.sched.com/event/9zs7/starting-with-apache-spark-best-practices-and-learning-from-the-field-felix-cheung-microsoft <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachecon-startingwithapachesparkbestpractices-170520185427-thumbnail.jpg?width=120&height=120&fit=bounds" /> https://apachebigdata2017.sched.com/event/9zs7/starting-with-apache-spark-best-practices-and-learning-from-the-field-felix-cheung-microsoft

Apache: Big Data - Starting with Apache Spark, Best Practices from felixcss

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0 Interactive Data Science From Scratch with Apache Zeppelin and Apache Spark /slideshow/interactive-data-science-from-scratch-with-apache-zeppelin-and-apache-spark/62117396 apachebigdatacon2016-interactivedatasciencefromscratch-160517210518
Apache: Big Data North America 2016 session How do you find the needle in the haystack? With Big Data, finding insight is a big problem. Visualization and exploratory analysis help convert on insights and Apache Zeppelin (incubating) is an essential tool for that. In this tutorial, Felix Cheung will introduce you to Apache Zeppelin, and provide step-by-step guides to get you up-and-running with Apache Zeppelin to run Big Data analysis with Apache Spark. This is going to be a heavily hands-on session, no previous experience with Zeppelin, Data Science, or Statistics necessary. ]]>
Apache: Big Data North America 2016 session How do you find the needle in the haystack? With Big Data, finding insight is a big problem. Visualization and exploratory analysis help convert on insights and Apache Zeppelin (incubating) is an essential tool for that. In this tutorial, Felix Cheung will introduce you to Apache Zeppelin, and provide step-by-step guides to get you up-and-running with Apache Zeppelin to run Big Data analysis with Apache Spark. This is going to be a heavily hands-on session, no previous experience with Zeppelin, Data Science, or Statistics necessary. ]]> Tue, 17 May 2016 21:05:18 GMT /slideshow/interactive-data-science-from-scratch-with-apache-zeppelin-and-apache-spark/62117396 felixcss@slideshare.net(felixcss) Interactive Data Science From Scratch with Apache Zeppelin and Apache Spark felixcss Apache: Big Data North America 2016 session How do you find the needle in the haystack? With Big Data, finding insight is a big problem. Visualization and exploratory analysis help convert on insights and Apache Zeppelin (incubating) is an essential tool for that. In this tutorial, Felix Cheung will introduce you to Apache Zeppelin, and provide step-by-step guides to get you up-and-running with Apache Zeppelin to run Big Data analysis with Apache Spark. This is going to be a heavily hands-on session, no previous experience with Zeppelin, Data Science, or Statistics necessary. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apachebigdatacon2016-interactivedatasciencefromscratch-160517210518-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache: Big Data North America 2016 session How do you find the needle in the haystack? With Big Data, finding insight is a big problem. Visualization and exploratory analysis help convert on insights and Apache Zeppelin (incubating) is an essential tool for that. In this tutorial, Felix Cheung will introduce you to Apache Zeppelin, and provide step-by-step guides to get you up-and-running with Apache Zeppelin to run Big Data analysis with Apache Spark. This is going to be a heavily hands-on session, no previous experience with Zeppelin, Data Science, or Statistics necessary.

Interactive Data Science From Scratch with Apache Zeppelin and Apache Spark from felixcss

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0 SparkR + Zeppelin /slideshow/sparkr-zeppelin-52650031/52650031 sparkrzeppelin-150910222105-lva1-app6892
An introduction to SparkR and using R/SparkR with Apache Zeppelin]]>
An introduction to SparkR and using R/SparkR with Apache Zeppelin]]> Thu, 10 Sep 2015 22:21:05 GMT /slideshow/sparkr-zeppelin-52650031/52650031 felixcss@slideshare.net(felixcss) SparkR + Zeppelin felixcss An introduction to SparkR and using R/SparkR with Apache Zeppelin <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkrzeppelin-150910222105-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> An introduction to SparkR and using R/SparkR with Apache Zeppelin

SparkR + Zeppelin from felixcss

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0 Sparkly Notebook: Interactive Analysis and Visualization with Spark /slideshow/sparkly-notebook-interactive-analysis-and-visualization-with-spark/47183865 sparkmeetup-150420031004-conversion-gate02
Sparkly Notebook: Interactive Analysis and Visualization with Spark - presented Apr 15, 2015 at Seattle Spark Meetup http://www.meetup.com/Seattle-Spark-Meetup/events/208711962/ ]]>
Sparkly Notebook: Interactive Analysis and Visualization with Spark - presented Apr 15, 2015 at Seattle Spark Meetup http://www.meetup.com/Seattle-Spark-Meetup/events/208711962/ ]]> Mon, 20 Apr 2015 03:10:03 GMT /slideshow/sparkly-notebook-interactive-analysis-and-visualization-with-spark/47183865 felixcss@slideshare.net(felixcss) Sparkly Notebook: Interactive Analysis and Visualization with Spark felixcss Sparkly Notebook: Interactive Analysis and Visualization with Spark - presented Apr 15, 2015 at Seattle Spark Meetup http://www.meetup.com/Seattle-Spark-Meetup/events/208711962/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkmeetup-150420031004-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Sparkly Notebook: Interactive Analysis and Visualization with Spark - presented Apr 15, 2015 at Seattle Spark Meetup http://www.meetup.com/Seattle-Spark-Meetup/events/208711962/

Sparkly Notebook: Interactive Analysis and Visualization with Spark from felixcss

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/pydatascalabledatasciencepythonrspark-170708220340-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/scalable-data-science-in-python-and-r-on-apache-spark/77652668 Scalable Data Science ... https://cdn.slidesharecdn.com/ss_thumbnails/ss2017-ssr-170612081419-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ssr-structured-streaming-for-r-and-machine-learning/76857968 SSR: Structured Stream... https://cdn.slidesharecdn.com/ss_thumbnails/apachecon-startingwithapachesparkbestpractices-170520185427-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/apache-big-data-starting-with-apache-spark-best-practices/76162282 Apache: Big Data - Sta...