�ݺ�ߣshows by User: JenAman

�ݺ�ߣshows by User: JenAman / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JenAman / Thu, 26 Oct 2017 08:49:53 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JenAman Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia /slideshow/deep-learning-and-streaming-in-apache-spark-2x-with-matei-zaharia/81232653 sseu-2017-val-171026084953
2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.]]>
2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.]]> Thu, 26 Oct 2017 08:49:53 GMT /slideshow/deep-learning-and-streaming-in-apache-spark-2x-with-matei-zaharia/81232653 JenAman@slideshare.net(JenAman) Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia JenAman 2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sseu-2017-val-171026084953-thumbnail.jpg?width=120&height=120&fit=bounds" /> 2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia from Jen Aman

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0 Snorkel: Dark Data and Machine Learning with Christopher Ré /slideshow/snorkel-dark-data-and-machine-learning-with-christopher-r/76732230 k2christopherr-170607144225
Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.]]>
Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.]]> Wed, 07 Jun 2017 14:42:25 GMT /slideshow/snorkel-dark-data-and-machine-learning-with-christopher-r/76732230 JenAman@slideshare.net(JenAman) Snorkel: Dark Data and Machine Learning with Christopher Ré JenAman Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/k2christopherr-170607144225-thumbnail.jpg?width=120&height=120&fit=bounds" /> Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.

Snorkel: Dark Data and Machine Learning with Christopher R辿 from Jen Aman

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0 Deep Learning on Apache® Spark™: Workflows and Best Practices /slideshow/deep-learning-on-apache-spark-workflows-and-best-practices-75721372/75721372 webinardeeplearningandapachesparkfinal-170505210704
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.]]>
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.]]> Fri, 05 May 2017 21:07:03 GMT /slideshow/deep-learning-on-apache-spark-workflows-and-best-practices-75721372/75721372 JenAman@slideshare.net(JenAman) Deep Learning on Apache® Spark™: Workflows and Best Practices JenAman The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webinardeeplearningandapachesparkfinal-170505210704-thumbnail.jpg?width=120&height=120&fit=bounds" /> The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™: Workflows and Best Practices from Jen Aman

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0 Deep Learning on Apache® Spark™ : Workflows and Best Practices /slideshow/deep-learning-on-apache-spark-workflows-and-best-practices/75721324 webinardeeplearningandapachesparkfinal-170505210503
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.]]>
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.]]> Fri, 05 May 2017 21:05:03 GMT /slideshow/deep-learning-on-apache-spark-workflows-and-best-practices/75721324 JenAman@slideshare.net(JenAman) Deep Learning on Apache® Spark™ : Workflows and Best Practices JenAman The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webinardeeplearningandapachesparkfinal-170505210503-thumbnail.jpg?width=120&height=120&fit=bounds" /> The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™ : Workflows and Best Practices from Jen Aman

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0 RISELab:Enabling Intelligent Real-Time Decisions /slideshow/riselabenabling-intelligent-realtime-decisions/71974860 k5ionstoica-170209191235
Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.]]>
Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.]]> Thu, 09 Feb 2017 19:12:35 GMT /slideshow/riselabenabling-intelligent-realtime-decisions/71974860 JenAman@slideshare.net(JenAman) RISELab:Enabling Intelligent Real-Time Decisions JenAman Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/k5ionstoica-170209191235-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab:Enabling Intelligent Real-Time Decisions from Jen Aman

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0 Spatial Analysis On Histological Images Using Spark /slideshow/spatial-analysis-on-histological-images-using-spark/63065474 8pweiyicheng-160614192234
Spark Summit 2016 talk by Wei-Yi Cheng (Roche Innovation Center)]]>
Spark Summit 2016 talk by Wei-Yi Cheng (Roche Innovation Center)]]> Tue, 14 Jun 2016 19:22:33 GMT /slideshow/spatial-analysis-on-histological-images-using-spark/63065474 JenAman@slideshare.net(JenAman) Spatial Analysis On Histological Images Using Spark JenAman Spark Summit 2016 talk by Wei-Yi Cheng (Roche Innovation Center) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/8pweiyicheng-160614192234-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Wei-Yi Cheng (Roche Innovation Center)

Spatial Analysis On Histological Images Using Spark from Jen Aman

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0 Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forecasts /slideshow/massive-simulations-in-spark-distributed-monte-carlo-for-global-health-forecasts/63065440 7pkyleforeman-160614192143
Spark Summit 2016 talk by Kyle Foreman]]>
Spark Summit 2016 talk by Kyle Foreman]]> Tue, 14 Jun 2016 19:21:43 GMT /slideshow/massive-simulations-in-spark-distributed-monte-carlo-for-global-health-forecasts/63065440 JenAman@slideshare.net(JenAman) Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forecasts JenAman Spark Summit 2016 talk by Kyle Foreman <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7pkyleforeman-160614192143-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Kyle Foreman

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forecasts from Jen Aman

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0 A Graph-Based Method For Cross-Entity Threat Detection /slideshow/a-graphbased-method-for-crossentity-threat-detection/63065374 6pkwongyan-160614192010
Spark Summit 2016 talk by Ping Yan (Salesforce.com) Herman Kwong]]>
Spark Summit 2016 talk by Ping Yan (Salesforce.com) Herman Kwong]]> Tue, 14 Jun 2016 19:20:10 GMT /slideshow/a-graphbased-method-for-crossentity-threat-detection/63065374 JenAman@slideshare.net(JenAman) A Graph-Based Method For Cross-Entity Threat Detection JenAman Spark Summit 2016 talk by Ping Yan (Salesforce.com) Herman Kwong <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/6pkwongyan-160614192010-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Ping Yan (Salesforce.com) Herman Kwong

A Graph-Based Method For Cross-Entity Threat Detection from Jen Aman

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0 Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark /slideshow/yggdrasil-faster-decision-trees-using-column-partitioning-in-spark/63065320 5pfirasabuzaid-160614191849
Spark Summit 2016 talk by Firas Abuzaid (MIT)]]>
Spark Summit 2016 talk by Firas Abuzaid (MIT)]]> Tue, 14 Jun 2016 19:18:49 GMT /slideshow/yggdrasil-faster-decision-trees-using-column-partitioning-in-spark/63065320 JenAman@slideshare.net(JenAman) Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark JenAman Spark Summit 2016 talk by Firas Abuzaid (MIT) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5pfirasabuzaid-160614191849-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Firas Abuzaid (MIT)

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark from Jen Aman

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0 Time-Evolving Graph Processing On Commodity Clusters /slideshow/timeevolving-graph-processing-on-commodity-clusters/63065243 4panandiyer-160614191710
Spark Summit 2016 talk by Anand Iyer]]>
Spark Summit 2016 talk by Anand Iyer]]> Tue, 14 Jun 2016 19:17:10 GMT /slideshow/timeevolving-graph-processing-on-commodity-clusters/63065243 JenAman@slideshare.net(JenAman) Time-Evolving Graph Processing On Commodity Clusters JenAman Spark Summit 2016 talk by Anand Iyer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/4panandiyer-160614191710-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Anand Iyer

Time-Evolving Graph Processing On Commodity Clusters from Jen Aman

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0 Deploying Accelerators At Datacenter Scale Using Spark /slideshow/deploying-accelerators-at-datacenter-scale-using-spark/63065203 3phuangwu-160614191603
Spark Summit 2016 talk by Di Wu (UCLA & Falcon Computing Solutions, Inc.) and Muhuan Huang (UCLA & Falcon Computing Solutions, Inc.)]]>
Spark Summit 2016 talk by Di Wu (UCLA & Falcon Computing Solutions, Inc.) and Muhuan Huang (UCLA & Falcon Computing Solutions, Inc.)]]> Tue, 14 Jun 2016 19:16:02 GMT /slideshow/deploying-accelerators-at-datacenter-scale-using-spark/63065203 JenAman@slideshare.net(JenAman) Deploying Accelerators At Datacenter Scale Using Spark JenAman Spark Summit 2016 talk by Di Wu (UCLA & Falcon Computing Solutions, Inc.) and Muhuan Huang (UCLA & Falcon Computing Solutions, Inc.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3phuangwu-160614191603-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Di Wu (UCLA & Falcon Computing Solutions, Inc.) and Muhuan Huang (UCLA & Falcon Computing Solutions, Inc.)

Deploying Accelerators At Datacenter Scale Using Spark from Jen Aman

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0 Re-Architecting Spark For Performance Understandability /slideshow/rearchitecting-spark-for-performance-understandability-63065166/63065166 2pkayousterhout-160614191511
Spark Summit talk by Kay Ousterhout (UC Berkeley)]]>
Spark Summit talk by Kay Ousterhout (UC Berkeley)]]> Tue, 14 Jun 2016 19:15:11 GMT /slideshow/rearchitecting-spark-for-performance-understandability-63065166/63065166 JenAman@slideshare.net(JenAman) Re-Architecting Spark For Performance Understandability JenAman Spark Summit talk by Kay Ousterhout (UC Berkeley) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2pkayousterhout-160614191511-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit talk by Kay Ousterhout (UC Berkeley)

Re-Architecting Spark For Performance Understandability from Jen Aman

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0 Re-Architecting Spark For Performance Understandability /slideshow/rearchitecting-spark-for-performance-understandability/63065132 2pkayousterhout-160614191417
Spark Summit 2016 talk by Kay Ousterhout (UC Berkeley)]]>
Spark Summit 2016 talk by Kay Ousterhout (UC Berkeley)]]> Tue, 14 Jun 2016 19:14:17 GMT /slideshow/rearchitecting-spark-for-performance-understandability/63065132 JenAman@slideshare.net(JenAman) Re-Architecting Spark For Performance Understandability JenAman Spark Summit 2016 talk by Kay Ousterhout (UC Berkeley) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2pkayousterhout-160614191417-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Kay Ousterhout (UC Berkeley)

Re-Architecting Spark For Performance Understandability from Jen Aman

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0 Low Latency Execution For Apache Spark /slideshow/low-latency-execution-for-apache-spark/63065065 1ppandavendataraman-160614191233
Spark Summit 2016 talk by Shivaram Venkataraman (UC Berkeley) and Aurojit Panda (UC Berkeley)]]>
Spark Summit 2016 talk by Shivaram Venkataraman (UC Berkeley) and Aurojit Panda (UC Berkeley)]]> Tue, 14 Jun 2016 19:12:33 GMT /slideshow/low-latency-execution-for-apache-spark/63065065 JenAman@slideshare.net(JenAman) Low Latency Execution For Apache Spark JenAman Spark Summit 2016 talk by Shivaram Venkataraman (UC Berkeley) and Aurojit Panda (UC Berkeley) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1ppandavendataraman-160614191233-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Shivaram Venkataraman (UC Berkeley) and Aurojit Panda (UC Berkeley)

Low Latency Execution For Apache Spark from Jen Aman

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0 Efficient State Management With Spark 2.0 And Scale-Out Databases /slideshow/efficient-state-management-with-spark-20-and-scaleout-databases/63064963 9imozafariramnarayan-160614190939
Spark Summit 2016 talk by Jags Ramnarayan (Snappydata) and Barzan Mozafari (University of Michigan)]]>
Spark Summit 2016 talk by Jags Ramnarayan (Snappydata) and Barzan Mozafari (University of Michigan)]]> Tue, 14 Jun 2016 19:09:38 GMT /slideshow/efficient-state-management-with-spark-20-and-scaleout-databases/63064963 JenAman@slideshare.net(JenAman) Efficient State Management With Spark 2.0 And Scale-Out Databases JenAman Spark Summit 2016 talk by Jags Ramnarayan (Snappydata) and Barzan Mozafari (University of Michigan) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9imozafariramnarayan-160614190939-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Jags Ramnarayan (Snappydata) and Barzan Mozafari (University of Michigan)

Efficient State Management With Spark 2.0 And Scale-Out Databases from Jen Aman

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0 Livy: A REST Web Service For Apache Spark /slideshow/livy-a-rest-web-service-for-apache-spark/63064913 8iiyermittal-160614190821
Spark Summit 2016 talk by Anand Iyer (Cloudera) and Pravin Mittal (Microsoft Corporation)]]>
Spark Summit 2016 talk by Anand Iyer (Cloudera) and Pravin Mittal (Microsoft Corporation)]]> Tue, 14 Jun 2016 19:08:21 GMT /slideshow/livy-a-rest-web-service-for-apache-spark/63064913 JenAman@slideshare.net(JenAman) Livy: A REST Web Service For Apache Spark JenAman Spark Summit 2016 talk by Anand Iyer (Cloudera) and Pravin Mittal (Microsoft Corporation) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/8iiyermittal-160614190821-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Anand Iyer (Cloudera) and Pravin Mittal (Microsoft Corporation)

Livy: A REST Web Service For Apache Spark from Jen Aman

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0 GPU Computing With Apache Spark And Python /slideshow/gpu-computing-with-apache-spark-and-python-63064880/63064880 7isiukwanlam-160614190731
Spark Summit 2016 talk by Siu Kwan Lam (Continuum Analytics)]]>
Spark Summit 2016 talk by Siu Kwan Lam (Continuum Analytics)]]> Tue, 14 Jun 2016 19:07:31 GMT /slideshow/gpu-computing-with-apache-spark-and-python-63064880/63064880 JenAman@slideshare.net(JenAman) GPU Computing With Apache Spark And Python JenAman Spark Summit 2016 talk by Siu Kwan Lam (Continuum Analytics) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7isiukwanlam-160614190731-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Siu Kwan Lam (Continuum Analytics)

GPU Computing With Apache Spark And Python from Jen Aman

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0 Spark And Cassandra: 2 Fast, 2 Furious /slideshow/spark-and-cassandra-2-fast-2-furious-63064852/63064852 6irussellspitzer-160614190651
Spark Summit 2016 talk by Russell Spitzer (DataStax)]]>
Spark Summit 2016 talk by Russell Spitzer (DataStax)]]> Tue, 14 Jun 2016 19:06:45 GMT /slideshow/spark-and-cassandra-2-fast-2-furious-63064852/63064852 JenAman@slideshare.net(JenAman) Spark And Cassandra: 2 Fast, 2 Furious JenAman Spark Summit 2016 talk by Russell Spitzer (DataStax) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/6irussellspitzer-160614190651-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Russell Spitzer (DataStax)

Spark And Cassandra: 2 Fast, 2 Furious from Jen Aman

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0 Building Custom Machine Learning Algorithms With Apache SystemML /slideshow/building-custom-machine-learning-algorithms-with-apache-systemml/63064784 5ifredreiss-160614190446
Spark Summit 2016 talk by Frederick Reiss (IBM)]]>
Spark Summit 2016 talk by Frederick Reiss (IBM)]]> Tue, 14 Jun 2016 19:04:45 GMT /slideshow/building-custom-machine-learning-algorithms-with-apache-systemml/63064784 JenAman@slideshare.net(JenAman) Building Custom Machine Learning Algorithms With Apache SystemML JenAman Spark Summit 2016 talk by Frederick Reiss (IBM) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5ifredreiss-160614190446-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 talk by Frederick Reiss (IBM)

Building Custom Machine Learning Algorithms With Apache SystemML from Jen Aman

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0 Spark on Mesos /slideshow/spark-on-mesos/63064731 4ichenwampler-160614190314
Spark Summit 2016 Talk by Tim Chen and Dean Wampler (Lightbend)]]>
Spark Summit 2016 Talk by Tim Chen and Dean Wampler (Lightbend)]]> Tue, 14 Jun 2016 19:03:14 GMT /slideshow/spark-on-mesos/63064731 JenAman@slideshare.net(JenAman) Spark on Mesos JenAman Spark Summit 2016 Talk by Tim Chen and Dean Wampler (Lightbend) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/4ichenwampler-160614190314-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark Summit 2016 Talk by Tim Chen and Dean Wampler (Lightbend)

Spark on Mesos from Jen Aman

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/sseu-2017-val-171026084953-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-learning-and-streaming-in-apache-spark-2x-with-matei-zaharia/81232653 Deep Learning and Stre... https://cdn.slidesharecdn.com/ss_thumbnails/k2christopherr-170607144225-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/snorkel-dark-data-and-machine-learning-with-christopher-r/76732230 Snorkel: Dark Data and... https://cdn.slidesharecdn.com/ss_thumbnails/webinardeeplearningandapachesparkfinal-170505210704-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-learning-on-apache-spark-workflows-and-best-practices-75721372/75721372 Deep Learning on Apach...