Scalable Data Science and Deep Learning with H2O
GOTO Conference Chicago May 12 2015
http://gotocon.com/chicago-2015/speaker/Arno+Candel
H2O is fast scalable open-source machine learning and deep learning for Smarter Applications. Using in-memory compression techniques, H2O can handle billions of data rows in-memory — even on small compute clusters. The platform includes interfaces for R, Python, Scala, Java, JS and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. H2O was built alongside (and on top of) both Hadoop and Spark clusters and is deployed within minutes. Sparkling Water combines the flexibility of Spark with the speed and accuracy of H2O's Machine Learning solution.
In this talk, we explain H2O's scalable in-memory architecture and design principles and outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. We will cover data ingest, feature engineering, model tuning, model validation and model selection; and how to take models into production. Live demos will be run on distributed systems. By the end of this presentation, you will know how to create your own machine learning models on your data using R, Python (iPython Notebooks) or Flow.
Bio:
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelera
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Arno candel scalabledatascienceanddeeplearningwithh2o_reworkboston2015Sri Ambati
https://www.re-work.co/events/deep-learning-boston-2015
Scalable Data Science & Deep Learning with H2O
H2O is fast scalable open-source machine learning and deep learning for Smarter Applications. Using in-memory compression techniques, H2O can handle billions of data rows in-memory — even on small compute clusters. The platform includes interfaces for R, Python, Scala, Java, JS and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. H2O was built alongside (and on top of) both Hadoop and Spark clusters and is deployed within minutes. Sparkling Water combines the flexibility of Spark with the speed and accuracy of H2O's Machine Learning solution.
In this workshop, we explain H2O's scalable in-memory architecture and design principles and outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. We cover data ingest, feature engineering, model tuning, model validation and model selection; and how to take models into production. Live demos will be run on distributed systems. By the end of this workshop, you will know how to create your own machine learning models on your data using R, Python (iPython Notebooks) or Flow.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.
Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Arno candel scalabledatascienceanddeeplearningwithh2o_odsc_boston2015Sri Ambati
http://opendatascicon.com/schedule/scalable-data-science-and-deep-learning-with-h2o/
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
MLconf - Distributed Deep Learning for Classification and Regression Problems...Sri Ambati
Video recording (no audio?): http://new.livestream.com/accounts/7874891/events/3565981/videos/68114143 from 32:00 to 54:30
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice for highest predictive performance in traditional business analytics. This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of business-critical problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization, parameter tuning and a fully-featured R interface. World record performance on the classic MNIST dataset, best-in-class accuracy for a high-dimensional eBay text classification problem and other relevant datasets showcase the power of this game-changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
Bio:
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O Open Source Deep Learning, Arno Candel 03-20-14Sri Ambati
More information in our Deep Learning webinar: http://www.slideshare.net/0xdata/h2-o-deeplearningarnocandel052114
Latest slide deck: http://www.slideshare.net/0xdata/h2o-distributed-deep-learning-by-arno-candel-071614
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Alex Tellez's slides on Deep Learning Applications, including using auto-encoders, finding better Bordeaux wine, and fighting crime in Chicago, from the 3/11/15 Meetup at H2O.ai HQ and the 3/12/15 Meetup at Mills College.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep Water - Bringing Tensorflow, Caffe, Mxnet to H2OSri Ambati
Arno Candel introduces Deep Water, which brings Tensorflow, Caffe, Mxnet to H2O. It also brings support for GPUs, image classification, NLP and much more to H2O.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Arno candel scalabledatascienceanddeeplearningwithh2o_reworkboston2015Sri Ambati
https://www.re-work.co/events/deep-learning-boston-2015
Scalable Data Science & Deep Learning with H2O
H2O is fast scalable open-source machine learning and deep learning for Smarter Applications. Using in-memory compression techniques, H2O can handle billions of data rows in-memory — even on small compute clusters. The platform includes interfaces for R, Python, Scala, Java, JS and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. H2O was built alongside (and on top of) both Hadoop and Spark clusters and is deployed within minutes. Sparkling Water combines the flexibility of Spark with the speed and accuracy of H2O's Machine Learning solution.
In this workshop, we explain H2O's scalable in-memory architecture and design principles and outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. We cover data ingest, feature engineering, model tuning, model validation and model selection; and how to take models into production. Live demos will be run on distributed systems. By the end of this workshop, you will know how to create your own machine learning models on your data using R, Python (iPython Notebooks) or Flow.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.
Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine. Follow him on Twitter: @ArnoCandel.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Arno candel scalabledatascienceanddeeplearningwithh2o_odsc_boston2015Sri Ambati
http://opendatascicon.com/schedule/scalable-data-science-and-deep-learning-with-h2o/
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
MLconf - Distributed Deep Learning for Classification and Regression Problems...Sri Ambati
Video recording (no audio?): http://new.livestream.com/accounts/7874891/events/3565981/videos/68114143 from 32:00 to 54:30
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice for highest predictive performance in traditional business analytics. This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of business-critical problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization, parameter tuning and a fully-featured R interface. World record performance on the classic MNIST dataset, best-in-class accuracy for a high-dimensional eBay text classification problem and other relevant datasets showcase the power of this game-changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
Bio:
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O Open Source Deep Learning, Arno Candel 03-20-14Sri Ambati
More information in our Deep Learning webinar: http://www.slideshare.net/0xdata/h2-o-deeplearningarnocandel052114
Latest slide deck: http://www.slideshare.net/0xdata/h2o-distributed-deep-learning-by-arno-candel-071614
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Alex Tellez's slides on Deep Learning Applications, including using auto-encoders, finding better Bordeaux wine, and fighting crime in Chicago, from the 3/11/15 Meetup at H2O.ai HQ and the 3/12/15 Meetup at Mills College.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep Water - Bringing Tensorflow, Caffe, Mxnet to H2OSri Ambati
Arno Candel introduces Deep Water, which brings Tensorflow, Caffe, Mxnet to H2O. It also brings support for GPUs, image classification, NLP and much more to H2O.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Project “Deep Water” (H2O integration with other deep learning libraries - Jo...Data Science Milan
The “Deep Water" project is about integrating our H2O platform with other open-source deep learning libraries such as TensorFlow, mxnet and Caffe. I will talk about the motivation and potential benefits of this project and then carry out a live demo using mxnet as the GPU backend.
Everything has its pros and cons, and machine learning algorithms are not an exception. We will discuss several cases where classical methods of machine learning prove to be more effective than neural networks. We will emphasize on the importance of a comprehensive view of machine learning, and rational approach to each problem.
Updates on the Fake Object Pipeline for HSC Survey Song Huang
This is the presentation for the development of SynPipe on Feb 2016. SynPipe is a synthetic object pipeline to test the photometric performance of the HSC survey. SynPipe can be found here: https://github.com/dr-guangtou/synpipe
February 2017 HUG: Data Sketches: A required toolkit for Big Data AnalyticsYahoo Developer Network
In the analysis of big data there are problematic queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most frequent items, joins, matrix computations, and graph analysis. If approximate results are acceptable, there is a class of sub-linear, stochastic streaming algorithms, called "sketches", that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of extracting results for these problem queries in real-time, sketches are the only known solution. For any analysis system that requires these problematic queries from big data, sketches are a required toolkit that should be tightly integrated into the system's analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours, or minutes to seconds on a number of its internal platforms. This talk covers the current state of our Open Source DataSketches.github.io library, which includes adaptations and example code for Pig, Hive, Spark and Druid and gives architectural examples of use and a case study.
Speakers:
Jon Malkin is a scientist at Yahoo working to extend the DataSketches library. His previous roles have involved large scale data processing for sponsored search, display advertising, user counting, ad targeting, and cross-device user identity modeling.
Alexander Saydakov is a senior software engineer at Yahoo working on the open source Data Sketches project. In his previous roles he has been involved in building large-scale back-end data processing systems and frameworks for data analytics and experimentation based on Torque, Hadoop, Pig, Hive and Druid. Alexander’s education background is in the field of applied mathematics.
Machine Learning for Smarter Apps - Jacksonville MeetupSri Ambati
Machine Learning for Smarter Apps with Tom Kraljevic
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
In this decl from HiPEAC 2018 in Manchester, CERN's Maria Girona outlines computing challenges at the Large Hadron Collider (LHC).
"The Large Hadron Collider (LHC) is one of the largest and most complicated scientific apparata ever constructed. The detectors at the LHC ring see as many as 800 million proton-proton collisions per second. An event in 10 to the 11th power is new physics and there is a hierarchical series of steps to extract a tiny signal from an enormous background. High energy physics (HEP) has long been a driver in managing and processing enormous scientific datasets and the largest scale high throughput computing centers. HEP developed one of the first scientific computing grids that now regularly operates 750k processor cores and half of an exabyte of disk storage located on 5 continents including hundred of connected facilities. In this keynote, I will discuss the challenges of capturing, storing and processing the large volumes of data generated at CERN. I will also discuss how these challenges will evolve towards the High-Luminosity Large Hadron Collider (HL-LHC), the upgrade programme scheduled to begin taking data in 2026 and to run into the 2030s, generating some 30 times more data than the LHC has currently produced."
Watch the video: https://wp.me/p3RLHQ-i4s
Learn more: https://www.hipeac.net/2018/manchester/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Astronomical Data Processing on the LSST Scale with Apache SparkDatabricks
The next decade promises to be exciting for both astronomy and computer science with a number of large-scale astronomical surveys in preparation. One of the most important ones is Large Scale Survey Telescope, or LSST. LSST will produce the first ‘video’ of the deep sky in history by continually scanning the visible sky and taking one 3.2 giga-pixel image every 20 seconds. In this talk we will describe LSST’s unique design and how its image processing pipeline produces catalogs of astronomical objects. To process and quickly cross-match catalog data we built AXS (Astronomy Extensions for Spark), a system based on Apache Spark. We will explain its design and what is behind its great cross-matching performance.
Hadoop 2.0, and in particular YARN has opened up a lot of potential applications beyond MapReduce. This presentation explains some of the ways this happened, and what you can now do that you couldn't before. It also introduces some new tools (Spark) and infrastructure pieces (Mesos) to achieve even more efficient cluster use.
With the HPC Cloud facility, SURFsara offers self-service, dynamically scalable and fully configurable HPC systems to the Dutch academic community. Users have, for example, a free choice of operating system and software.
The HPC Cloud offers full control over a HPC cluster, with fast CPUs and high memory nodes and it is possible to attach terabytes of local storage to a compute node. Because of this flexibility, users can fully tailor the system for a particular application. Long-running and small compute jobs are equally welcome. Additionally, the system facilitates collaboration: users can share control over their virtual private HPC cluster with other users and share processing time, data and results. A portal with wiki, fora, repositories, issue system, etc. is offered for collaboration projects as well.
"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
"The Arrival of Quantum Computing" by Will ZengImpact.Tech
Slides from the Impact.tech seminar about quantum computing, given by Will Zeng. The presentation addresses the technologies, the actors, and the market around quantum computing.
If there is a sense of reality, there must be a sense of possibility.
Impact.tech Launch Seminars are meant to give entrepreneurs and investors a launch into a topic where they can apply their skills to make a major positive impact for humanity and the world.
Slides from Matt Dowle's presentation at H2O Open Tour: NYC
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Project “Deep Water” (H2O integration with other deep learning libraries - Jo...Data Science Milan
The “Deep Water" project is about integrating our H2O platform with other open-source deep learning libraries such as TensorFlow, mxnet and Caffe. I will talk about the motivation and potential benefits of this project and then carry out a live demo using mxnet as the GPU backend.
Everything has its pros and cons, and machine learning algorithms are not an exception. We will discuss several cases where classical methods of machine learning prove to be more effective than neural networks. We will emphasize on the importance of a comprehensive view of machine learning, and rational approach to each problem.
Updates on the Fake Object Pipeline for HSC Survey Song Huang
This is the presentation for the development of SynPipe on Feb 2016. SynPipe is a synthetic object pipeline to test the photometric performance of the HSC survey. SynPipe can be found here: https://github.com/dr-guangtou/synpipe
February 2017 HUG: Data Sketches: A required toolkit for Big Data AnalyticsYahoo Developer Network
In the analysis of big data there are problematic queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most frequent items, joins, matrix computations, and graph analysis. If approximate results are acceptable, there is a class of sub-linear, stochastic streaming algorithms, called "sketches", that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of extracting results for these problem queries in real-time, sketches are the only known solution. For any analysis system that requires these problematic queries from big data, sketches are a required toolkit that should be tightly integrated into the system's analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours, or minutes to seconds on a number of its internal platforms. This talk covers the current state of our Open Source DataSketches.github.io library, which includes adaptations and example code for Pig, Hive, Spark and Druid and gives architectural examples of use and a case study.
Speakers:
Jon Malkin is a scientist at Yahoo working to extend the DataSketches library. His previous roles have involved large scale data processing for sponsored search, display advertising, user counting, ad targeting, and cross-device user identity modeling.
Alexander Saydakov is a senior software engineer at Yahoo working on the open source Data Sketches project. In his previous roles he has been involved in building large-scale back-end data processing systems and frameworks for data analytics and experimentation based on Torque, Hadoop, Pig, Hive and Druid. Alexander’s education background is in the field of applied mathematics.
Machine Learning for Smarter Apps - Jacksonville MeetupSri Ambati
Machine Learning for Smarter Apps with Tom Kraljevic
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
In this decl from HiPEAC 2018 in Manchester, CERN's Maria Girona outlines computing challenges at the Large Hadron Collider (LHC).
"The Large Hadron Collider (LHC) is one of the largest and most complicated scientific apparata ever constructed. The detectors at the LHC ring see as many as 800 million proton-proton collisions per second. An event in 10 to the 11th power is new physics and there is a hierarchical series of steps to extract a tiny signal from an enormous background. High energy physics (HEP) has long been a driver in managing and processing enormous scientific datasets and the largest scale high throughput computing centers. HEP developed one of the first scientific computing grids that now regularly operates 750k processor cores and half of an exabyte of disk storage located on 5 continents including hundred of connected facilities. In this keynote, I will discuss the challenges of capturing, storing and processing the large volumes of data generated at CERN. I will also discuss how these challenges will evolve towards the High-Luminosity Large Hadron Collider (HL-LHC), the upgrade programme scheduled to begin taking data in 2026 and to run into the 2030s, generating some 30 times more data than the LHC has currently produced."
Watch the video: https://wp.me/p3RLHQ-i4s
Learn more: https://www.hipeac.net/2018/manchester/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Astronomical Data Processing on the LSST Scale with Apache SparkDatabricks
The next decade promises to be exciting for both astronomy and computer science with a number of large-scale astronomical surveys in preparation. One of the most important ones is Large Scale Survey Telescope, or LSST. LSST will produce the first ‘video’ of the deep sky in history by continually scanning the visible sky and taking one 3.2 giga-pixel image every 20 seconds. In this talk we will describe LSST’s unique design and how its image processing pipeline produces catalogs of astronomical objects. To process and quickly cross-match catalog data we built AXS (Astronomy Extensions for Spark), a system based on Apache Spark. We will explain its design and what is behind its great cross-matching performance.
Hadoop 2.0, and in particular YARN has opened up a lot of potential applications beyond MapReduce. This presentation explains some of the ways this happened, and what you can now do that you couldn't before. It also introduces some new tools (Spark) and infrastructure pieces (Mesos) to achieve even more efficient cluster use.
With the HPC Cloud facility, SURFsara offers self-service, dynamically scalable and fully configurable HPC systems to the Dutch academic community. Users have, for example, a free choice of operating system and software.
The HPC Cloud offers full control over a HPC cluster, with fast CPUs and high memory nodes and it is possible to attach terabytes of local storage to a compute node. Because of this flexibility, users can fully tailor the system for a particular application. Long-running and small compute jobs are equally welcome. Additionally, the system facilitates collaboration: users can share control over their virtual private HPC cluster with other users and share processing time, data and results. A portal with wiki, fora, repositories, issue system, etc. is offered for collaboration projects as well.
"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
"The Arrival of Quantum Computing" by Will ZengImpact.Tech
Slides from the Impact.tech seminar about quantum computing, given by Will Zeng. The presentation addresses the technologies, the actors, and the market around quantum computing.
If there is a sense of reality, there must be a sense of possibility.
Impact.tech Launch Seminars are meant to give entrepreneurs and investors a launch into a topic where they can apply their skills to make a major positive impact for humanity and the world.
Slides from Matt Dowle's presentation at H2O Open Tour: NYC
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Sparkling Water Webinar October 29th, 2014Sri Ambati
Sparkling Water is the newest application on the Apache Spark in-memory platform to extend Machine Learning for better predictions and to quickly deploy models into production. H2O is proud to partner with Cloudera and Databricks to bring this capability to a wide audience.
H2O is for data scientists and business analysts who need scalable and fast machine learning. H2O is an open source predictive analytics platform. Unlike traditional analytics tools, H2O provides a combination of extraordinary math and high performance parallel processing with unrivaled ease of use. H2O speaks the language of data science with support for R, Python, Scala, Java and a robust REST API. Smart business applications are powered by H2O’s NanoFast¬TM Scoring Engine. Learn more by going to http://www.h2o.ai and contact us for more information.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
In this talk we will share the idea of developing self guiding application that would provide the most engaging user experience possible using crowd sourced knowledge on a mobile interface. We will discuss and share how historical usage data could be mined using machine learning to identify application usage patterns to generate probable next actions. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning, Deep Learning
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O is the open source math & machine learning engine for big data that brings distribution and parallelism to powerful algorithms while keeping the widely used languages of R and JSON as an API. H2O brings and elegant lego-like infrastructure that brings fine-grained parallelism to math over simple distributed arrays.
Jeff will showcase the sparklyr the new R package to interface with Spark and talk about the different use extensions including the rsparkling ML package.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
We present solutions on how to make the cyberspace secure through feature-rich, robust, yet lean machine learning-based algorithms that help organizations identify malicious actors, intruders and illegal system access by studying features that arise purely from system login behavior.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
We'll look under the hood of R2D3, a project aimed at giving people without a technical background a sense of what machine learning is. R2D3 received a Vizzie from the National Science Foundation. The project was also an honoree in the 2016 Webby Awards.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
How Sparkling Water brings Fast Scalable Machine learning via H2O to Apache Spark.
By Michal Malohlava and H2O.ai
Our 100th Meetup at 0xdata, September 30, 2014
Open Source meets Out Door.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep Learning for Public Safety in Chicago and San FranciscoSri Ambati
Presentation on Deep Learning for Public Safety using open data sets from the cities of San Francisco and Chicago.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Tony will talk about creating interfaces and experiences for AI which bring interpretability and context to data products.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Scalable Data Science and Deep Learning with H2Oodsc
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Deep Learning in the Wild with Arno CandelSri Ambati
"Deep Learning in the Wild" Meetup at H2O, Mountain View
Livestream: http://t.co/o7p2hYcWgy (includes part 2 with Alex Tellez)
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
In this deck, Torsten Hoefler from ETH Zurich presents: Data-Centric Parallel Programming.
"The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating code definition from its optimization. We show how to tune several applications in this model and IR. Furthermore, we show a global, datacentric view of a state-of-the-art quantum transport simulator to optimize its execution on supercomputers. The approach yields coarse and fine-grained data-movement characteristics, which are used for performance and communication modeling, communication avoidance, and data-layout transformations. The transformations are tuned for the Piz Daint and Summit supercomputers, where each platform requires different caching and fusion strategies to perform optimally. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code."
Watch the video: https://wp.mep3RLHQ-kup
Learn more: http://htor.inf.ethz.ch
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Stay up-to-date with the OpenACC Monthly Highlights. February's edition covers the updated specification OpenACC 3.2, upcoming GPU Hackathons and Bootcamps, OpenACC's BOF at SC21 , recent research, new resources and more!
San Francisco Hadoop User Group Meetup Deep LearningSri Ambati
Hadoop User Group, San Francisco, Dec 10 2014.
Video: http://new.livestream.com/accounts/10932136/events/3649553 (starting at 48 minutes)
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice for highest predictive performance in traditional business analytics. This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of business-critical problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization, parameter tuning and a fully-featured R interface. World record performance on the classic MNIST dataset, best-in-class accuracy for a high-dimensional eBay text classification problem and other relevant datasets showcase the power of this game-changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
Bio:
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Slides for my talk at Cloud Foundry Summit Europe 2016.
Nearly 1.2 million people die in road crashes each year (WHO - 2015) with additional millions becoming injured or disabled. One big part of this problem is worst road traffic conditions and unless action is taken, road traffic injuries are predicted to become the fifth leading cause of death by 2030. Moreover, although road traffic injuries have been a major cause of mortality for many years, most traffic accidents are both predictable and preventable. In this talk, we want to demonstrate a scalable IoT platform that uses weather data and data from other cars to warn drivers of dangerous conditions. We will show how CF can help to save human lives and the architecture behind this. Additionally, we will also explain the data science that is involved.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
"Comparing Variable Importance from Ensemble and Deep Learning Methods for AdTech Data"
Variable Importance brings interpretability to popular black box modeling techniques. In this talk we study performance of popular ensemble techniques like Random Forest, Gradient Boosting with GLM. We observe certain traits that get magnified by non-linear techniques like Deep Learning that are otherwise missed by GBM or Random Forest.
We describe Open Source Scalable Machine Learning package, H2O which through ease-of-use and speed makes comparisons and picking best-of-breed and ensembles more natural. H2O's implementation of these algorithms tracks popular open source and text book implementations closely.
Stay up-to-date on the latest news, events and resources for the OpenACC community. This month’s highlights covers the first remote GPU Hackathons, a complete schedule of upcoming events, using OpenACC for a biophysics problem, NVIDIA HPC SDK, GCC 10, new resources and more!
Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member
Apache Beam (incubating) is a programming model and library for unified batch & streaming big data processing. This talk will cover the Beam programming model broadly, including its origin story and vision for the future. We will dig into how Beam separates concerns for authors of streaming data processing pipelines, isolating what you want to compute from where your data is distributed in time and when you want to produce output. Time permitting, we might dive deeper into what goes into building a Beam runner, for example atop Apache Apex.
OpenACC and Open Hackathons Monthly Highlights: July 2022.pptxOpenACC
Stay up-to-date with the OpenACC and Open Hackathons Monthly Highlights. July’s edition covers the 2022 OpenACC and Hackathons Summit, NVIDIA’s Applied Research Accelerator Program, upcoming Open Hackathons and Bootcamps, recent research, new resources, and more!
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingPaco Nathan
London Spark Meetup 2014-11-11 @Skimlinks
http://www.meetup.com/Spark-London/events/217362972/
To paraphrase the immortal crooner Don Ho: "Tiny Batches, in the wine, make me happy, make me feel fine." http://youtu.be/mlCiDEXuxxA
Apache Spark provides support for streaming use cases, such as real-time analytics on log files, by leveraging a model called discretized streams (D-Streams). These "micro batch" computations operated on small time intervals, generally from 500 milliseconds up. One major innovation of Spark Streaming is that it leverages a unified engine. In other words, the same business logic can be used across multiple uses cases: streaming, but also interactive, iterative, machine learning, etc.
This talk will compare case studies for production deployments of Spark Streaming, emerging design patterns for integration with popular complementary OSS frameworks, plus some of the more advanced features such as approximation algorithms, and take a look at what's ahead — including the new Python support for Spark Streaming that will be in the upcoming 1.2 release.
Also, let's chat a bit about the new Databricks + O'Reilly developer certification for Apache Spark…
Top 10 Performance Gotchas for scaling in-memory Algorithms.srisatish ambati
Top 10 Data Parallelism and Model Parallelism lessons from scaling H2O.
"Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm."
Similar to ArnoCandelScalabledatascienceanddeeplearningwithh2o_gotochg (20)
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...Sri Ambati
Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai
H2O Open Source GenAI World SF 2023
In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.
Patrick Hall, Professor, AI Risk Management, The George Washington University
H2O Open Source GenAI World SF 2023
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!
Dr. Alexy Khrabrov, Open Source Science Community Director, IBM
H2O Open Source GenAI World SF 2023
In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Michelle Tanco, Head of Product, H2O.ai
H2O Open Source GenAI World SF 2023
Learn how the makers at H2O.ai are building internal tools to solve real use cases using H2O Wave and h2oGPT. We will walk through an end-to-end use case and discuss how to incorporate business rules and generated content to rapidly develop custom AI apps using only Python APIs.
Applied Gen AI for the Finance Vertical Sri Ambati
Megan Kurka, Vice President, Customer Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...Sri Ambati
Pascal Pfeiffer, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.
Introducción al Aprendizaje Automatico con H2O-3 (1)Sri Ambati
En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...Sri Ambati
Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto.
In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
2. H2O.ai
Machine Intelligence
Who Am I?
Arno Candel
Chief Architect,
Physicist & Hacker at H2O.ai
PhD Physics, ETH Zurich 2005
10+ yrs Supercomputing (HPC)
6 yrs at SLAC (Stanford Lin. Accel.)
3.5 yrs Machine Learning
1.5 yrs at H2O.ai
Fortune Magazine
Big Data All Star 2014
Follow me @ArnoCandel 2
3. H2O.ai
Machine Intelligence
Outline
• Introduction
• H2O Deep Learning Architecture
• Live Demos:
Flow GUI - Airline Logistic Regression
Scoring Engine - Million Songs Classification
R - MNIST Unsupervised Anomaly Detection
Flow GUI - Higgs Boson Classification
Sparkling Water - Chicago Crime Prediction
iPython - CitiBike Demand Prediction
• Outlook
3
4. H2O.ai
Machine Intelligence
In-Memory ML
Distributed
Open Source
APIs
4
Memory-Efficient Data Structures
Cutting-Edge Algorithms
Use all your Data (No Sampling)
Accuracy with Speed and Scale
Ownership of Methods - Apache V2
Easy to Deploy: Bare, Hadoop, Spark, etc.
Java, Scala, R, Python, JavaScript, JSON
NanoFast Scoring Engine (POJO)
H2O - Product Overview
6. H2O.ai
Machine Intelligence
Community & Install Base
103 634 2789
463 2,887 13,237
Companies
Users
Mar 2014 July 2014 Mar 2015
Active Users
150+
6
ML is the new SQL
Prediction is the new Search
8. H2O.ai
Machine Intelligence
8
Ad Optimization (200% CPA Lift with H2O)
P2B Model Factory (60k models,
15x faster with H2O than before)
Fraud Detection (11% higher accuracy with
H2O Deep Learning - saves millions)
…and many large insurance and financial
services companies!
Real-time marketing (H2O is 10x faster than
anything else)
Actual Customer Use Cases
9. H2O.ai
Machine Intelligence
9
H2O - Easy Installation
Today’s live demos will be run on yesterday’s nightly
build of the next-gen product (alpha, in QA)!
h2o.ai/download
Try it out!
10. H2O.ai
Machine Intelligence
10
8 nodes on EC2: all cores active.
Trains on 116M rows in 17 seconds!
Demo: GLM (Elastic Net) on Airlines
Eastern Airlines: Always on time (No schedule!)
11. H2O.ai
Machine Intelligence
11
Example: First GBM tree
Fast and easy path to production (batch or real-time)!
POJO Scoring Engine
Standalone Java scoring code is auto-generated!
Note:
no heap allocation,
pure decision-making
12. H2O.ai
Machine Intelligence
Multi-layer feed-forward Neural Network
Trained with back-propagation (SGD, AdaDelta)
+ distributed processing for big data
(fine-grain in-memory MapReduce on distributed data)
+ multi-threaded speedup
(async fork/join worker threads operate at FORTRAN speeds)
+ smart algorithms for fast & accurate results
(automatic standardization, one-hot encoding of categoricals, missing value imputation, weight &
bias initialization, adaptive learning rate, momentum, dropout/l1/L2 regularization, grid search,
N-fold cross-validation, checkpointing, load balancing, auto-tuning, model averaging, etc.)
= powerful tool for (un)supervised machine
learning on real-world data
12
H2O Deep Learning
all 320 cores maxed out
13. H2O.ai
Machine Intelligence
threads: async
13
H2O Deep Learning Architecture
K-V
K-V
HTTPD
HTTPD
nodes/JVMs: sync
communication
w
w w
w w w w
w1
w3 w2
w4
w2+w4
w1+w3
w* = (w1+w2+w3+w4)/4
map:
each node trains a copy of
the weights and biases with
(some* or all of) its local
data with asynchronous F/J
threads
initial model: weights and biases w
updated model: w*
H2O in-memory non-
blocking hash map:
K-V store
reduce:
model averaging: average weights
and biases from all nodes,
speedup is at least #nodes/
log(#rows)
http://arxiv.org/abs/1209.4129
Keep iterating over the data (“epochs”), score at user-given times
Query & display the
model via JSON, WWW
2
2 4
31
1
1
1
4
3 2
1 2
1
i
*auto-tuned (default) or user-
specified number of rows per
MapReduce iteration
Main Loop:
14. H2O.ai
Machine Intelligence
14
Standard 60k/10k data
No distortions
No convolutions
No unsupervised training
No ensemble
10 hours on 10 16-core servers
World-record!
0.83% test set error
H2O Deep Learning beats MNIST
Handwritten digits: 28^2=784 gray-scale pixel values
A Classic Benchmark!
16. H2O.ai
Machine Intelligence
16
Images courtesy CERN / LHC
Higgs
vs
Background
Large Hadron Collider: Largest experiment of mankind!
$13+ billion, 16.8 miles long, 120 MegaWatts, -456F, 1PB/day, etc.
Higgs boson discovery (July ’12) led to 2013 Nobel prize!
HIGGS UCI Dataset:
21 low-level features AND
7 high-level derived features (physics formulae)
Train: 10M rows, Valid: 500k, Test: 500k rows
Higgs Boson - Classification Problem
17. H2O.ai
Machine Intelligence
17
? ? ?
Former baseline for AUC: 0.733 and 0.816
H2O Algorithm low-level H2O AUC all features H2O AUC
Generalized Linear Model 0.596 0.684
Random Forest 0.764 0.840
Gradient Boosted Trees 0.753 0.839
Neural Net 1 hidden layer 0.760 0.830
H2O Deep Learning ?
add derived features
Deep Learning for Higgs Detection?
18. H2O.ai
Machine Intelligence
18
Deep DL model on 21
low-level features
(no physics formula
based extra features)
valid 500k rows
test 500k rows
train 10M rows
10 nodes:
AUC = 0.84 after 40 mins
AUC = 0.87+ after 8 hours
H2O Deep Learning Higgs Demo
Deep Learning learns Physics!
26. H2O.ai
Machine Intelligence
26
Build H2O Deep Learning Model
Train a H2O Deep Learning
Model on Data obtained by
Spark SQL Query
Predict whether Arrest will be
made with AUC of 0.90+
35. H2O.ai
Machine Intelligence
Mark Landry (joined H2O!) will hold a master class on May 19!
http://www.meetup.com/Silicon-Valley-Big-Data-Science/events/222303884/
35
Competitive Data Science
fingers crossed!
38. H2O.ai
Machine Intelligence
Outlook - Algorithm Roadmap
• Ensembles (Erin LeDell et al.)
• Automatic Hyper-Parameter Tuning
• Convolutional Layers for Deep Learning
• Natural Language Processing: tf-idf, Word2Vec
• Generalized Low Rank Models
• PCA, SVD, K-Means, Matrix Factorization
• Recommender Systems
And many more!
38
Public JIRAs - Join H2O!
39. H2O.ai
Machine Intelligence
Key Take-Aways
H2O is an open source predictive analytics
platform for data scientists and business
analysts who need scalable, fast and
accurate machine learning.
H2O Deep Learning is ready to take your
advanced analytics to the next level.
Try it on your data!
39
https://github.com/h2oai
H2O Google Group
http://h2o.ai
@h2oai
Thank You!