Machine Learning for Big Data Analytics: Scaling In with Containers while Sc...Ian Lumb
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
Armed with nothing more than an Apache Spark toting laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results.
Of course, as soon as your prototyping proves successful, you'll want to scale out to embrace the volume, variety and velocity that characterizes today's Big Data demands... in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing Big Data requirements in production boils down to effectively and efficiently embracing containers and clusters for Big Data Analytics.
And this is where offerings from Univa shine - i.e., in making the transition from prototype to production completely seamless. For some use cases, it makes sense to scale-in Spark based applications within Docker containers via Univa Grid Engine Container Edition or Navops by Univa; whereas in others, Spark is interfaced (as a Mesos-compliant framework) with Univa Universal Resource Broker, to permit scaling out on a cluster. In both scenarios, your production Spark applications are scheduled alongside other classes of workload - without a need for dedicated resources.
Agenda:
• Overview of Apache Spark as a platform for Deep Learning - from Python-based Jupyter Notebooks to Spark's Machine Learning library MLlib
• Overview of prototyping Machine Learning via Apache Spark on a laptop - without and within Docker containers
• Introductions to Univa Grid Engine Container Edition and Univa Universal Resource Broker plus Navops by Univa
• Overview of production Big Data Analytics platforms for Machine Learning
• Docker-containerized Apache Spark and Univa Grid Engine Container Edition
• Docker-containerized Apache Spark and Navops by Univa
• Apache Spark plus Univa Universal Resource Broker
• Introducing support for GPUs without and within Docker containers
• Use case example - using Machine Learning to classify data from Twitter without and within Docker containers
• Summary and next steps
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
This document evaluates scheduling algorithms for applications in a cloud environment. It compares strict matchmaking-based algorithms like minimum execution time, minimum completion time, and maximum resource utilization to utility-driven algorithms that consider user satisfaction and partial requirement satisfaction. The evaluations are conducted using CloudSim, a cloud simulation tool, by modeling cloud resources, applications, and scheduling various workloads under different algorithms to analyze metrics like completion time and resource utilization. The results show that utility-driven algorithms that take user requirements into account perform better overall.
- In last few years, rapidly increasing businesses
and their capabilities & capacities in terms of computing has
grown in very large scale. To manage business requirements
High performance computing with very large scale resources is
required. Businesses do not want to invest & concentrate on
managing these computing issues rather than their core business.
Thus, they move to service providers. Service providers such as
data centers serve their clients by sharing resources for
computing, storage etc. and maintaining all those.
This document provides an overview and tutorial on using CloudSim, an open-source simulation toolkit for modeling and simulation of cloud computing infrastructures and applications. It discusses CloudSim's features and architecture, prerequisites for using it, and how to set up the development environment in Eclipse. Sample code examples are presented to demonstrate running simulations of data centers with hosts and cloudlets using CloudSim.
This document discusses Cloud2Sim, a new concurrent and distributed cloud simulation tool that extends CloudSim. Cloud2Sim leverages distributed execution and storage capabilities of in-memory data grids to allow cloud simulations to run in a distributed manner across multiple nodes. This improves upon existing cloud simulators that typically run sequentially on a single computer. The document describes Cloud2Sim's design, implementation, evaluations showing its ability to reduce simulation time, and outlines future work such as incorporating search capabilities and optimizing object sizes.
Este documento presenta una guía sobre metodología de investigación. Explica diferentes técnicas e instrumentos para la recolección de datos como observación, entrevistas y encuestas. También describe cómo evaluar la validez y confiabilidad de los instrumentos utilizados y el proceso de operacionalización de variables. Finalmente, introduce conceptos como hipótesis y análisis estadístico de resultados.
La Unión Europea ha acordado un paquete de sanciones contra Rusia por su invasión de Ucrania. Las sanciones incluyen restricciones a las transacciones con bancos rusos clave y la prohibición de la venta de aviones y equipos a Rusia. Los líderes de la UE esperan que las sanciones aumenten la presión económica sobre Rusia y la disuadan de continuar su agresión contra Ucrania.
This document discusses using drones and PostgreSQL/PostGIS for agricultural applications. It describes how drones can capture imaging data for tasks like measuring crop health through NDVI analysis. PostgreSQL is useful for organizing the large amounts of drone data, like flight plans, sensor readings, and imagery. The document provides an example of importing this data into PostgreSQL and using PostGIS functions to process imagery, extract waypoints of problem areas, and more.
Machine Learning for Big Data Analytics: Scaling In with Containers while Sc...Ian Lumb
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
Armed with nothing more than an Apache Spark toting laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results.
Of course, as soon as your prototyping proves successful, you'll want to scale out to embrace the volume, variety and velocity that characterizes today's Big Data demands... in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing Big Data requirements in production boils down to effectively and efficiently embracing containers and clusters for Big Data Analytics.
And this is where offerings from Univa shine - i.e., in making the transition from prototype to production completely seamless. For some use cases, it makes sense to scale-in Spark based applications within Docker containers via Univa Grid Engine Container Edition or Navops by Univa; whereas in others, Spark is interfaced (as a Mesos-compliant framework) with Univa Universal Resource Broker, to permit scaling out on a cluster. In both scenarios, your production Spark applications are scheduled alongside other classes of workload - without a need for dedicated resources.
Agenda:
• Overview of Apache Spark as a platform for Deep Learning - from Python-based Jupyter Notebooks to Spark's Machine Learning library MLlib
• Overview of prototyping Machine Learning via Apache Spark on a laptop - without and within Docker containers
• Introductions to Univa Grid Engine Container Edition and Univa Universal Resource Broker plus Navops by Univa
• Overview of production Big Data Analytics platforms for Machine Learning
• Docker-containerized Apache Spark and Univa Grid Engine Container Edition
• Docker-containerized Apache Spark and Navops by Univa
• Apache Spark plus Univa Universal Resource Broker
• Introducing support for GPUs without and within Docker containers
• Use case example - using Machine Learning to classify data from Twitter without and within Docker containers
• Summary and next steps
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
This document evaluates scheduling algorithms for applications in a cloud environment. It compares strict matchmaking-based algorithms like minimum execution time, minimum completion time, and maximum resource utilization to utility-driven algorithms that consider user satisfaction and partial requirement satisfaction. The evaluations are conducted using CloudSim, a cloud simulation tool, by modeling cloud resources, applications, and scheduling various workloads under different algorithms to analyze metrics like completion time and resource utilization. The results show that utility-driven algorithms that take user requirements into account perform better overall.
- In last few years, rapidly increasing businesses
and their capabilities & capacities in terms of computing has
grown in very large scale. To manage business requirements
High performance computing with very large scale resources is
required. Businesses do not want to invest & concentrate on
managing these computing issues rather than their core business.
Thus, they move to service providers. Service providers such as
data centers serve their clients by sharing resources for
computing, storage etc. and maintaining all those.
This document provides an overview and tutorial on using CloudSim, an open-source simulation toolkit for modeling and simulation of cloud computing infrastructures and applications. It discusses CloudSim's features and architecture, prerequisites for using it, and how to set up the development environment in Eclipse. Sample code examples are presented to demonstrate running simulations of data centers with hosts and cloudlets using CloudSim.
This document discusses Cloud2Sim, a new concurrent and distributed cloud simulation tool that extends CloudSim. Cloud2Sim leverages distributed execution and storage capabilities of in-memory data grids to allow cloud simulations to run in a distributed manner across multiple nodes. This improves upon existing cloud simulators that typically run sequentially on a single computer. The document describes Cloud2Sim's design, implementation, evaluations showing its ability to reduce simulation time, and outlines future work such as incorporating search capabilities and optimizing object sizes.
Este documento presenta una guía sobre metodología de investigación. Explica diferentes técnicas e instrumentos para la recolección de datos como observación, entrevistas y encuestas. También describe cómo evaluar la validez y confiabilidad de los instrumentos utilizados y el proceso de operacionalización de variables. Finalmente, introduce conceptos como hipótesis y análisis estadístico de resultados.
La Unión Europea ha acordado un paquete de sanciones contra Rusia por su invasión de Ucrania. Las sanciones incluyen restricciones a las transacciones con bancos rusos clave y la prohibición de la venta de aviones y equipos a Rusia. Los líderes de la UE esperan que las sanciones aumenten la presión económica sobre Rusia y la disuadan de continuar su agresión contra Ucrania.
This document discusses using drones and PostgreSQL/PostGIS for agricultural applications. It describes how drones can capture imaging data for tasks like measuring crop health through NDVI analysis. PostgreSQL is useful for organizing the large amounts of drone data, like flight plans, sensor readings, and imagery. The document provides an example of importing this data into PostgreSQL and using PostGIS functions to process imagery, extract waypoints of problem areas, and more.
Amgen Cowen and Company 37th Annual Health Care Conference PresentationThe ScientifiK
This document summarizes an oral presentation given by David Meline, Executive Vice President and Chief Financial Officer of Amgen, at the Cowen and Company 37th Annual Health Care Conference on March 8, 2017. The presentation discusses Amgen's strong financial and operational execution in 2016, advancement of its innovative pipeline including positive cardiovascular outcomes data for Repatha, and growth opportunities across therapeutic areas like oncology, neuroscience, and bone health. It also reviews Amgen's biosimilars portfolio and strategy to invest in external innovation and deliver significant returns to shareholders through dividends and share repurchases.
La creatividad puede y debe desarrollarse en todas las personas, no es solo un don. La escuela debe estimular la creatividad y las potencialidades de los niños y niñas. Los juegos de los niños pequeños muestran procesos creadores en cómo reconstruyen la cultura de acuerdo a sus intereses.
This document discusses Tachibana Waita and his involvement with UEC and Dentoo.LT. It describes his work on various open source projects including an otaku bot, Slack Palette, and contributions to MeCab. Waita aims to make the d250g2 Slack more public and accessible while using bots to enhance the experience. The document praises Waita's many technical accomplishments and contributions to open source over the years.
Artigo publicado em 2013 na IEEE na latim America foi ponto de partida para estudos da caracterização das redes PON. O artigo apresenta discute a situação na época, bem como o mercado e previsões de negócios.
El desastre de Bhopal ocurrió en 1984 cuando hubo una fuga masiva de gas tóxico en una planta química en Bhopal, India, propiedad de Union Carbide, exponiendo a 500,000 personas y causando miles de muertes inmediatas y daños a la salud a largo plazo. La fuga se debió a fallas en el mantenimiento y seguridad de la planta. El desastre es uno de los peores accidentes industriales de la historia y dejó un legado de contaminación y enfermedades.
Filipinos commonly view humanity through the lens of fate and family. They believe that one's station in life is determined by luck or fate, and bringing honor to one's family is essential. Additionally, Filipinos strongly believe in an afterlife and view people more by their province than accomplishments. Theologically, the document discusses that humanity is uniquely made in God's image but is also sinful by nature, and discusses the biblical views of the physical and immaterial aspects of human nature.
El documento describe los principios fundamentales de la enseñanza del idioma inglés. Se enfatiza la importancia de desarrollar las cuatro habilidades del idioma (comprensión auditiva, comprensión lectora, expresión oral y expresión escrita) a través de tareas comunicativas auténticas. También se incorporan elementos del Enfoque Comunicativo y otros enfoques que ponen énfasis en la comunicación, como el aprendizaje cooperativo y basado en contenidos. El objetivo es que los estudiantes aprendan inglés como una herram
La función cosecante es la función trigonométrica definida como el inverso de la función seno. La cosecante de un ángulo es igual a 1 dividido entre el seno del ángulo. La cosecante tiene un dominio de R - {kπ} y un recorrido de (-∞, -1] U [1, ∞). Se hace infinita cuando el seno es igual a cero y es útil en arquitectura para calcular alturas, ángulos y patrones geométricos en edificios.
Early adopters report "easier replication, faster deployment and lower configuration and operating costs" of applications that involve Docker containers - an open platform that allows developers and sysadmins to build, ship and execute distributed applications.
Not surprisingly then, a groundswell of organizations are interested in evaluating Docker containers in proof-of-concept initiatives and/or pilot projects. The transition to production use, however, introduces additional requirements as Docker containers need to be incorporated into existing IT infrastructures and (ultimately) integrated into application workflows.
In answering the 5 Ws and one H, the aim of this webinar is to provide a technical overview and demonstration of Docker and to frame its use within the context of High Performance Computing and Big Data Analytics.
Learn all about Docker.
Agenda:
• What are Docker containers - relative to physical machines, VMs and other containers?
• Who is responsible for Docker containers?
• Why and when were Docker containers created?
• What is the container ecosystem?
• Where is use of containers appropriate and not appropriate?
▸ HPC applications?
▸ Big Data Analytics? Specifically, Spark-based applications?
▸ On premise and in the cloud?
▸ Is running Docker different in HPC versus microservice-based applications?
• How can I make use of Docker containers?
▸ How can I containerize my application?
▸ How can I create, or make use of, a Docker image?
▸ How can I run Docker containers as I do other types of workloads?
• Getting Started and Next Steps
Speaker:
Ian Lumb, System Architect, Univa Corporation.
As an HPC specialist, Ian Lumb has spent about two decades at the global intersection of IT and science. Ian received his B.Sc. from Montreal's McGill University, and then an M.Sc. from York University in Toronto. Although his undergraduate and graduate studies emphasized geophysics, Ian's current interests include workload orchestration and container optimization for HPC to Big Data Analytics in clusters and clouds.
Video Download
Video is available in .mp4 format from http://www.univa.com/resources/webinar-docker101.php.
Brazilian Summer School in Machine Learning 2016
Day 2 - Lecture 4: Advanced Workflows: Feature Selection, Boosting, Gradient Descent, and Stacking
Lecturer: Dr. José Antonio Ortega - jao (BigML)
Modern Machine Learning Infrastructure and PracticesWill Gardella
Slides from Curtis Huang's talk at the Couchbase Meetup in Mountain View on August 18th. Curtis is a Senior Software Engineer at Facebook working on Machine Learning, with experience in both ad tech and search.
"AI and machine learning have transformed the technology industry for the last decade, creating a foundation for web search, ranking/recommendation, and object/speech recognition. In this talk, I will discuss a collection of machine learning approaches to effectively analyzing and modeling large-scale data. From a hands-on practitioner's perspective, I will talk about the process of building a ML pipeline from idea to production, the challenges, and lessons learned. As an example, I will describe the infrastructure and components of a modern ML ranking system."
Square's Machine Learning Infrastructure and Applications - Rong YanHakka Labs
1) Square uses machine learning for fraud detection in payments and to power recommendations on its Square Market platform.
2) Random forests and gradient boosted trees are the primary algorithms used for fraud detection, achieving up to a 10-11% improvement over random forests alone.
3) Square has built scalable machine learning infrastructure including parallel environments, data transport systems, and a learning management system to support rapid model development and evaluation.
This document discusses machine learning and Microsoft Azure ML Studio. It provides an overview of machine learning, the differences between traditional programming and machine learning, and the main types of machine learning problems. It then introduces Azure ML Studio as a web-based environment for building, testing, and deploying machine learning models using common algorithms and languages like R and Python. The document outlines the basic steps to build a machine learning solution and shows how Azure ML Studio allows publishing predictive models as APIs within minutes to apply to various applications and devices.
All major cloud service providers now have some ML offering. The startup costs are low-to-no. They provide seamless leverage of cloud resources for scale-ups = $’s.
Open source ML options are now common making the creation of very large models now possible. Open data sets are proliferating.
Presented at CF Machine Learning
Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics driven by data streams, technology advances, business needs and consumer demands, 3) the growth of in-memory analytics using tools like Alluxio and RocksDB, 4) rapid application development using APIs, notebooks, GUIs and microservices, 5) open sourcing of machine learning systems by tech giants, and 6) hybrid cloud computing models for deploying big data applications both on-premise and in the cloud.
Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics to enable real-time insights, and 3) leveraging in-memory technologies. He also covered 4) rapid application development tools, 5) open-sourcing of machine learning systems, and 6) hybrid cloud deployments of big data applications across on-premise and cloud environments.
This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics.
These are a few takeaways from this talk:
- Adopt Apache Beam for easier development and portability between Big Data Execution Engines.
- Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency.
- Accelerate your Big Data applications with In-Memory open source tools.
- Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices…
- Have Machine Learning part of your strategy or passively watch your industry completely transformed!
- How to advance your strategy for hybrid integration between cloud and on-premise deployments?
Cloud Native Night July 2019, Munich: Talk by Emil A. Siemes (@mesosphere, Principal Solution Engineer at Mesosphere)
=== Please download slides if blurred! ===
Abstract: Tired of managing infrastructure instead of creating exiting ml models? Learn what DC/OS can do for the data scientist.
Join us next time: https://www.meetup.com/Cloud-Native-muc/events
Amgen Cowen and Company 37th Annual Health Care Conference PresentationThe ScientifiK
This document summarizes an oral presentation given by David Meline, Executive Vice President and Chief Financial Officer of Amgen, at the Cowen and Company 37th Annual Health Care Conference on March 8, 2017. The presentation discusses Amgen's strong financial and operational execution in 2016, advancement of its innovative pipeline including positive cardiovascular outcomes data for Repatha, and growth opportunities across therapeutic areas like oncology, neuroscience, and bone health. It also reviews Amgen's biosimilars portfolio and strategy to invest in external innovation and deliver significant returns to shareholders through dividends and share repurchases.
La creatividad puede y debe desarrollarse en todas las personas, no es solo un don. La escuela debe estimular la creatividad y las potencialidades de los niños y niñas. Los juegos de los niños pequeños muestran procesos creadores en cómo reconstruyen la cultura de acuerdo a sus intereses.
This document discusses Tachibana Waita and his involvement with UEC and Dentoo.LT. It describes his work on various open source projects including an otaku bot, Slack Palette, and contributions to MeCab. Waita aims to make the d250g2 Slack more public and accessible while using bots to enhance the experience. The document praises Waita's many technical accomplishments and contributions to open source over the years.
Artigo publicado em 2013 na IEEE na latim America foi ponto de partida para estudos da caracterização das redes PON. O artigo apresenta discute a situação na época, bem como o mercado e previsões de negócios.
El desastre de Bhopal ocurrió en 1984 cuando hubo una fuga masiva de gas tóxico en una planta química en Bhopal, India, propiedad de Union Carbide, exponiendo a 500,000 personas y causando miles de muertes inmediatas y daños a la salud a largo plazo. La fuga se debió a fallas en el mantenimiento y seguridad de la planta. El desastre es uno de los peores accidentes industriales de la historia y dejó un legado de contaminación y enfermedades.
Filipinos commonly view humanity through the lens of fate and family. They believe that one's station in life is determined by luck or fate, and bringing honor to one's family is essential. Additionally, Filipinos strongly believe in an afterlife and view people more by their province than accomplishments. Theologically, the document discusses that humanity is uniquely made in God's image but is also sinful by nature, and discusses the biblical views of the physical and immaterial aspects of human nature.
El documento describe los principios fundamentales de la enseñanza del idioma inglés. Se enfatiza la importancia de desarrollar las cuatro habilidades del idioma (comprensión auditiva, comprensión lectora, expresión oral y expresión escrita) a través de tareas comunicativas auténticas. También se incorporan elementos del Enfoque Comunicativo y otros enfoques que ponen énfasis en la comunicación, como el aprendizaje cooperativo y basado en contenidos. El objetivo es que los estudiantes aprendan inglés como una herram
La función cosecante es la función trigonométrica definida como el inverso de la función seno. La cosecante de un ángulo es igual a 1 dividido entre el seno del ángulo. La cosecante tiene un dominio de R - {kπ} y un recorrido de (-∞, -1] U [1, ∞). Se hace infinita cuando el seno es igual a cero y es útil en arquitectura para calcular alturas, ángulos y patrones geométricos en edificios.
Early adopters report "easier replication, faster deployment and lower configuration and operating costs" of applications that involve Docker containers - an open platform that allows developers and sysadmins to build, ship and execute distributed applications.
Not surprisingly then, a groundswell of organizations are interested in evaluating Docker containers in proof-of-concept initiatives and/or pilot projects. The transition to production use, however, introduces additional requirements as Docker containers need to be incorporated into existing IT infrastructures and (ultimately) integrated into application workflows.
In answering the 5 Ws and one H, the aim of this webinar is to provide a technical overview and demonstration of Docker and to frame its use within the context of High Performance Computing and Big Data Analytics.
Learn all about Docker.
Agenda:
• What are Docker containers - relative to physical machines, VMs and other containers?
• Who is responsible for Docker containers?
• Why and when were Docker containers created?
• What is the container ecosystem?
• Where is use of containers appropriate and not appropriate?
▸ HPC applications?
▸ Big Data Analytics? Specifically, Spark-based applications?
▸ On premise and in the cloud?
▸ Is running Docker different in HPC versus microservice-based applications?
• How can I make use of Docker containers?
▸ How can I containerize my application?
▸ How can I create, or make use of, a Docker image?
▸ How can I run Docker containers as I do other types of workloads?
• Getting Started and Next Steps
Speaker:
Ian Lumb, System Architect, Univa Corporation.
As an HPC specialist, Ian Lumb has spent about two decades at the global intersection of IT and science. Ian received his B.Sc. from Montreal's McGill University, and then an M.Sc. from York University in Toronto. Although his undergraduate and graduate studies emphasized geophysics, Ian's current interests include workload orchestration and container optimization for HPC to Big Data Analytics in clusters and clouds.
Video Download
Video is available in .mp4 format from http://www.univa.com/resources/webinar-docker101.php.
Brazilian Summer School in Machine Learning 2016
Day 2 - Lecture 4: Advanced Workflows: Feature Selection, Boosting, Gradient Descent, and Stacking
Lecturer: Dr. José Antonio Ortega - jao (BigML)
Modern Machine Learning Infrastructure and PracticesWill Gardella
Slides from Curtis Huang's talk at the Couchbase Meetup in Mountain View on August 18th. Curtis is a Senior Software Engineer at Facebook working on Machine Learning, with experience in both ad tech and search.
"AI and machine learning have transformed the technology industry for the last decade, creating a foundation for web search, ranking/recommendation, and object/speech recognition. In this talk, I will discuss a collection of machine learning approaches to effectively analyzing and modeling large-scale data. From a hands-on practitioner's perspective, I will talk about the process of building a ML pipeline from idea to production, the challenges, and lessons learned. As an example, I will describe the infrastructure and components of a modern ML ranking system."
Square's Machine Learning Infrastructure and Applications - Rong YanHakka Labs
1) Square uses machine learning for fraud detection in payments and to power recommendations on its Square Market platform.
2) Random forests and gradient boosted trees are the primary algorithms used for fraud detection, achieving up to a 10-11% improvement over random forests alone.
3) Square has built scalable machine learning infrastructure including parallel environments, data transport systems, and a learning management system to support rapid model development and evaluation.
This document discusses machine learning and Microsoft Azure ML Studio. It provides an overview of machine learning, the differences between traditional programming and machine learning, and the main types of machine learning problems. It then introduces Azure ML Studio as a web-based environment for building, testing, and deploying machine learning models using common algorithms and languages like R and Python. The document outlines the basic steps to build a machine learning solution and shows how Azure ML Studio allows publishing predictive models as APIs within minutes to apply to various applications and devices.
All major cloud service providers now have some ML offering. The startup costs are low-to-no. They provide seamless leverage of cloud resources for scale-ups = $’s.
Open source ML options are now common making the creation of very large models now possible. Open data sets are proliferating.
Presented at CF Machine Learning
Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics driven by data streams, technology advances, business needs and consumer demands, 3) the growth of in-memory analytics using tools like Alluxio and RocksDB, 4) rapid application development using APIs, notebooks, GUIs and microservices, 5) open sourcing of machine learning systems by tech giants, and 6) hybrid cloud computing models for deploying big data applications both on-premise and in the cloud.
Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics to enable real-time insights, and 3) leveraging in-memory technologies. He also covered 4) rapid application development tools, 5) open-sourcing of machine learning systems, and 6) hybrid cloud deployments of big data applications across on-premise and cloud environments.
This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics.
These are a few takeaways from this talk:
- Adopt Apache Beam for easier development and portability between Big Data Execution Engines.
- Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency.
- Accelerate your Big Data applications with In-Memory open source tools.
- Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices…
- Have Machine Learning part of your strategy or passively watch your industry completely transformed!
- How to advance your strategy for hybrid integration between cloud and on-premise deployments?
Cloud Native Night July 2019, Munich: Talk by Emil A. Siemes (@mesosphere, Principal Solution Engineer at Mesosphere)
=== Please download slides if blurred! ===
Abstract: Tired of managing infrastructure instead of creating exiting ml models? Learn what DC/OS can do for the data scientist.
Join us next time: https://www.meetup.com/Cloud-Native-muc/events
This whitepaper details the use of High Performance Computing HPC in Aerospace & Defense, Earth Sciences, Education And Research, Financial Services among others...
IEEE Paper - A Study Of Cloud Computing Environments For High Performance App...Angela Williams
This document discusses cloud computing environments for high performance applications. It begins with an introduction to high performance computing and how cloud computing can provide scalable resources for HPC applications at a lower cost compared to traditional on-premise HPC systems. It then discusses different types of HPC applications and their requirements in more detail. The document also examines cluster-based HPC systems and Google's architecture for HPC in the cloud. It provides a performance analysis of several HPC cloud vendors and concludes with case studies of running HPC applications in the cloud.
This document provides an introduction to high performance computing (HPC) on Amazon Web Services (AWS). It discusses what HPC is and how grids and clusters are commonly used to enable parallel processing. It also outlines a wide range of HPC applications and how they map to AWS features. Key factors that make AWS compelling for HPC are its scalability, agility, ability to enable global collaboration, and cost optimization options. Sample HPC architectures that can be implemented on AWS including grid and cluster computing are also presented.
The document provides instructions for setting up a cluster using Ubuntu and MPICH for parallel computing. It discusses prerequisites like installing MPICH, SSH and GCC on multiple nodes. It describes how to define hostnames, set up authorized keys for passwordless communication, and create a machine file specifying processes on each node. The document also shows how to write a sample MPI program, compile it using MPICH and execute across nodes using the machine file.
Security Requirements and Security Threats In Layers Cloud and Security Issue...Editor IJCATR
Euacalyptus, OpenNebula and Nimbus are three major open-source cloud-computing software platforms. The overall
function of these systems is to manage the provisioning of virtual machines for a cloud providing infrastructure-as-a-service. These
various open-source projects provide an important alternative for those who do not wish to use a commercially provide cloud. This is a
fundamental concept in cloud computing, providing resources to deliver infrastructure as a service cloud customers, making users have
to buy and maintain computing resources and storage. In other hand, cloud service providers to provide better resources and facilities
customers need to know they are using cloud infrastructure services. In this end, we intend to security threats in the cloud layer and
then to analyse the security services in cloud computing infrastructure as a service to pay
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
The document provides details about an OpenPOWER and AI workshop being held on June 18-19, 2018 at the Barcelona Supercomputing Center.
Day 1 will provide an introduction to AI and cover topics like Power9 and PowerAI features, large model support, and use case demonstrations. Day 2 will focus on deeper learning exercises and industry use cases using Power9 features like distributed deep learning.
The agenda lists out the schedule and topics to be covered each day, including welcome sessions, technical presentations, breaks and wrap-up discussions.
This document provides an overview of cloud computing from researchers at UC Berkeley. It defines cloud computing as both software delivered as a service over the internet (SaaS) and the hardware/software in data centers that provide those services. When data center resources are provided on a pay-as-you-go basis to the public, it is considered utility computing or a public cloud. Private clouds refer to internal company data centers not available publicly. The researchers argue that large-scale commodity data centers offering resources at low costs have enabled cloud computing to provide services cheaper than medium-sized private data centers. They also discuss technical and business challenges and opportunities related to cloud computing.
This document provides an overview of cloud computing from researchers at UC Berkeley. It defines cloud computing as both software delivered as a service over the internet (SaaS) and the hardware/software in datacenters providing those services (clouds). The researchers argue that large, low-cost datacenters enabled cloud computing by lowering costs through economies of scale and statistical multiplexing. They classify current cloud offerings and discuss when utility computing is preferable to private clouds. The document identifies top obstacles to cloud computing growth and opportunities to overcome them.
RAPIDS is a suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.In this workshop, we will:
1. Introduce Rapids.ai & GPUs
2. Illustrate why GPUs are critical for machine learning and AI applications
3. Demonstrate common machine learning algorithms such as Regression, KNN,SGD etc. using RAPIDS on the QuSandbox
The document outlines a training course divided into 6 units covering various aspects of cloud computing including fundamentals, architecture, management, deployment models, service models, operating systems, virtualization, software development, networking, cloud service providers, and security. Unit 1 introduces cloud computing concepts, architecture, and management over 8 hours. Unit 2 covers cloud deployment and service models in 8 hours. Subsequent units address operating systems and virtualization, software development and networking, cloud service providers, and open source support and security, each for 8 hours.
This document summarizes a presentation on Adaptation as a Service (ADaaS). The presentation discusses the challenges of building autonomic management systems for complex distributed systems and proposes providing adaptation capabilities as a cloud service. It outlines several examples of how security, configuration, healing, and optimization capabilities could be delivered as services. The presentation also demonstrates a proof-of-concept system called Elascale that provides auto-scaling and self-healing as a service for dockerized applications on OpenStack.
Binh-Minh Nguyen presented an approach for migrating applications to interoperable clouds using a Cloud Abstraction Layer (CAL). CAL provides a generalized interface that abstracts away differences between cloud providers and allows applications to be deployed across multiple cloud platforms. It aims to address issues like vendor lock-in and allow easier migration of applications between clouds. A prototype was demonstrated using CAL to deploy a bioinformatics workflow as a service across OpenStack clouds.
International Conference on Utility and Cloud Computing December 9 – 12, Dres...Thomas Francis
HPC as a Service: benefits & challenges
HPC as a Service (in the Cloud) offers flexibility, business agility, scaling up and down, pay-per-use, OPEX instead CAPEX, but
It’s a new business and working paradigm Security, privacy, trust in service provider Intellectual property
Software Licensing
Heavy data transfers
www.theubercloud.com/hpc-as-a-service
Similar to Drilling Deep with Machine Learning as an Enterprise Enabled Micro Service (20)
Towards Deep Learning from Twitter for Improved Tsunami Alerts and AdvisoriesIan Lumb
Slides corresponding to an oral presentation made at the 2017 Fall Meeting of the American Geophysical Union in New Orleans, Louisiana. The full abstract can be found at https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/279699. The gist? Use is made of Natural Language Processing (NLP) to explore semantic similarities in word use for data extracted via Twitter.
Univa and SUSE at SC17: Scaling Machine Learning for SUSE Linux Containers, S...Ian Lumb
Armed with nothing more than an Apache Spark toting SUSE Linux laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results. Of course, as soon as your prototyping proves successful, you'll want to scale out to embrace the volume, variety and velocity that characterizes today's demands in Big Data Analytics ... in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing these ‘Big Data’ requirements in production boils down to effectively and efficiently embracing SUSE Linux containers, servers, clusters and clouds. As case studies will illustrate, this transition from prototype to production can be made successfully.
Managing Containerized HPC and AI Workloads on TSUBAME3.0Ian Lumb
By leveraging the operating environment provided by SUSE Linux Enterprise Server, Univa® Grid Engine® manages workloads for the Tokyo Institute of Technology’s (TITECH) TSUBAME3.0 supercomputer. In many respects, this is an exceptional supercomputer that combines the leading-edge compute (Intel Xeon CPUs and NVIDIA Pascal P100 GPUs) and interconnect (Intel Omni-Path) capabilities demanded by traditional HPC as well as Deep Learning workloads. TITECH’s desire to make extensive use of containerization via Docker is also a distinctive aspect of the overall platform that currently is being implemented. As noted in booth presentations at past SC events, SUSE Linux and Univa Grid Engine have a highly complementary affinity for Docker containers. In this year’s presentation, aspects of TSUBAME3.0 motivated projects for managing containerized workloads will be highlighted.
Univa Unicloud - High Volume Workloads: How Smart Companies are Harnessing th...Ian Lumb
Univa provides distributed resource management systems and tools for running high-volume workloads in cloud environments. Their key product, Univa Grid Engine, optimizes resource usage across data centers. Their Unicloud product automates deployment and scaling of workloads on public clouds. The presentation discussed use cases for bursting workloads to the cloud and running hybrid on-premises/cloud clusters. It demonstrated Unicloud's capabilities and provided best practices for cloud projects, including focusing on a single use case and addressing all prerequisites before using Unicloud.
Dev / Test / Ops – Gain More Horsepower and Reduce Costs by Sharing Kubernete...Ian Lumb
Containerization coupled with DevOps is revolutionizing application development and deployment, but organizations are creating silos of clusters that limit the operational efficiencies that can be gained by sharing hardware, software and systems administrators. This talk will cover how improved cluster management and consolidation at the orchestration, network and storage layers can yield great returns for developers, DevOps and IT management. (Slides from a presentation and demo at the Toronto Kubernetes Meetup on April 26, 2017.)
High Performance Computing in the Cloud is viable in numerous use cases. Common to all successful use cases for cloud-based HPC is the ability embrace latency. Not surprisingly then, early successes were achieved with embarrassingly parallel HPC applications involving minimal amounts of data - in other words, there was little or no latency to be hidden. Over the fulness of time, however, the HPC-cloud community has become increasingly adept in its ability to ‘hide’ latency and, in the process, support increasingly more sophisticated HPC use cases in public and private clouds. Real-world use cases, deemed relevant to remote sensing, will illustrate aspects of these sophistications for hiding latency in accounting for large volumes of data, the need to pass messages between simultaneously executing components of distributed-memory parallel applications, as well as (processing) workflows/pipelines. Finally, the impact of containerizing HPC for the cloud will be considered through the relatively recent creation of the Cloud Native Computing Foundation.
VoDcast Slides: The Rise in Popularity of Apache SparkIan Lumb
The document discusses the rise in popularity of Apache Spark. It highlights the key appealing aspect of Spark as its resilient distributed datasets (RDDs) which allow for fault-tolerant, parallel data structures that can be partitioned and manipulated via operators. Some key differences between Hadoop and Spark clusters are that Spark has its own cluster manager and supports a wider variety of applications by converging SQL, streaming, machine learning and graph analytics workloads. Applications that benefit from Spark include big data analytics and high-performance computing by decoupling from Hadoop.
Bright Topics Webinar April 15, 2015 - Modernized Monitoring for Cluster and ...Ian Lumb
Key takeaways:
How Bright Cluster Manager allows you to monitor HPC and Hadoop clusters
How Bright Cluster Manager allows you to monitor public and private clouds
How Bright Cluster Manager enables monitoring with alerts and health checks
How Bright Cluster Manager enables customized monitoring - including how to incorporate your own monitors
This recording (http://hubs.ly/y0JtjX0) includes live-product demonstrations of Bright Cluster Manager.
Utilizing Public AND Private Clouds with Bright Cluster ManagerIan Lumb
Slides corresponding to a webinar (http://hubs.ly/y0F-j80) given on March 25, 2015 for Bright Computing.
Key takeaways:
How Bright Cluster Manager allows you to seamlessly make use public clouds like Amazon Web Services (AWS)
How Bright Cluster Manager allows you to rapidly deploy a private cloud based on OpenStack
The recording (http://hubs.ly/y0F-j80) includes live-product demonstrations using Bright Cluster Manager.
How to Upgrade Your Hadoop Stack in 1 Step -- with Zero DowntimeIan Lumb
Outline:
- The Apache Project's 4-step upgrade process for its Hadoop distro
- Upgrade processes for the Hadoop stack involving Apache Ambari and other management tools
- Bright roles for Hadoop service definition, assignment and composition
- The 1-step, 0-downtime Bright upgrade process for Hadoop distros and the analytics stack
Bright Cluster Manager: A Comprehensive, Integrated Management Solution for P...Ian Lumb
Bright Cluster Manager is a comprehensive, integrated management solution for parallel computing resources both currently and in the future. It provisions, monitors, and manages heterogeneous computing resources including systems, storage, and interconnects. It provides a unified graphical user interface and command line for managing multiple clusters and clouds simultaneously. It simplifies development by providing tools, libraries, and workload management. It integrates Intel Xeon Phi coprocessors by packaging all necessary software and allowing them to be configured, controlled, and monitored through the management interface. It also performs health checks on Xeon Phis and only schedules jobs to nodes passing the checks.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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AI Fusion Buddy Review: Key Features
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✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
DDS-Security 1.2 - What's New? Stronger security for long-running systems
Drilling Deep with Machine Learning as an Enterprise Enabled Micro Service
1. www.univa.com
Ian Lumb, Solutions Architect
Disruptive Technology Track
Rice University Oil/Gas HPC Conference
March 15, 2017
Drilling Deep with
Machine Learning as
an Enterprise Enabled
Micro Service
2. Hypothesis
Container clusters are disruptive enablers of enterprise-
grade Machine Learning capabilities in oil/gas
applications and workflows when delivered as a fully
converged platform
2
www.univa.com
3. 3
Univa ML Survey: Key Findings
Most organizations have been using Machine Learning for more
than 2 years
Available infrastructure for Machine Learning remains CPU-heavy
There is interest in deploying new infrastructure to support Machine
Learning over the next 6 months
Machine Learning applications will make use of all capabilities -
existing CPUs & GPUs plus new Big Data & containerized
There is definitely interest in private/public/hybrid clouds – though
on-premise deployments are expected to dominate
www.univa.com
5. www.univa.com
5
Container Clusters for Machine Learning
Apache Spark is easily containerized as a service or an application
Navops Command delivers sophisticated, enterprise-grade
workload placement and advanced policy management capabilities
for Kubernetes-based container clusters that addresses mixed
workloads
Microservices-based approaches can be systematically refactored
into existing applications and/or workflows
Univa offers unique solutions for fully converged infrastructures
Main points
Always enjoy sharing our most-disruptive ideas at this event
Last year, we intro’d & promoted containerizing HPC applications – apps run in Docker containers, and are managed by UGE like any other kind of workload
Uptake continues to be strong – we have a number of PoCs underway with some moving towards production
Our intention is to focus this year’s DTT contribution on containers again, but in an even more disruptive way
Link to next slide: That being the case, a hypothesis seems like a reasonable place to initiative our disruptiveness
Main points:
Let’s start with a hypothesis for this year’s DTT contribution
We’re actually talking about clusters comprised entirely of containers – and this is quite a departure from the traditional HPC (built around UGE, for example) even for us!
Along with others in this container cluster ecosystem, we’re working expediently to deliver enterprise-grade capabilities to our customers and for our partners
There was an indication of interest in Machine Learning at last year’s RiceU event, and even more at this year’s
We thought it appropriate, then, to share use cases relating to ML
As you’ll learn/see in a moment, we have reason to believe that there is no single solution for introducing ML capabilities into the enterprise – so that apps and workflows might benefit from its introduction … and that is why we are hypothesizing that a fully converged platform is required to ensure that ML in container clusters makes good as a disruptive enabler
A fully converged platform refers to a single infrastructural element that supports a wide variety of use cases
Link to next slide: On the next slide we share some of the data that contributed to the formulation of this hypothesis
Main points:
Last August, we ran a webinar with a Machine Learning emphasis – it was (surprisingly) well attended and caused us to follow up with participants via a survey
Here we’ve summararized the key findings of that survey
ML’s been around for ~30 yrs, so perhaps it isn’t too surprising that many claimed to be using it for > 2 yrs
And although existing deployments were heavily slanted towards use of CPUs, there was interest in enhancing that infrastructure in the near term
Our survey, as well as anecdotal evidence from our ML-facing interactions with our prospects as well as our existing customers, suggests that there is no single capability that will be used to deliver ML capabilities even within a single organization – in other words, some will favor GPUs, while others Big Data offerings like Spark or based around Hadoop … and some of this will be containerized
And, not too surprisingly, many expressed interest in making use of the cloud in adopting ML capabilities
Given the interest in a broad and deep array of ML capabilities, converged platforms that can handle this high degree of variability are extremely attractive to those needing to provide ML capabilities
Link to next slide: To fix ideas, on the next slide we return to our hypothesis and share a specific use case
Fine Print
Small sample size bias challenges extrapolation to larger markets
North American and EMEA sampling bias challenges extrapolation to other geographies
New Intel Xeon Phi processor requires interpolation to determine its impact
Existing GPU capabilities likely to be repurposed to accommodate Machine Learning requirements
Main points:
On this slide, we’re sharing a converged platform based on Kubernetes – originally from Google and now open source, Kubernetes allows you to deploy container clusters
In this case, we’re emphasizing Machine Learning capabilities
On the left side, ML apps that only require CPUs can be run the ‘traditional way’ – perhaps as they are being now … with or without workload managers like Univa Grid Engine – these are the legacy, non-containerized workloads alluded to at the top of the slide
For the record, we’re not restricted to Machine Learning apps, as HPC apps could also be included
On the other side of the slide we’ve introduced capabilities based on Apache Spark that have been containerized … In one case, we’ve illustrated a completely containerized Spark application; whereas in the other, Spark is provided as a containerized service
Taken together, this is a scenario that supports mixed workloads – legacy, non-containerized alongside containerized … and that’s really making good on the promise of convergence
Importantly you can run your ML apps as they are or as they will be – and this includes an allowance for progressively refactoring in micro services based architectures or developing new apps with micro services in mind from the outset
Our Navops Command is a key enterprise enabler as it introduces the ability to place workload according to policies – policies, and variants thereof, of policies you are likely familiar with from workload managers in an HPC context … please see our poster for a more-complete description of the policies Command brings to Kubernetes container clusters
Finally, Command works with vanilla, open-source Kubernetes or with enhanced distributions such as Red Hat OpenShift
Link to next slide: On the next slide, we wrap by sharing our conclusions
The Unique Capabilities of Navops Command
Workload prioritization
Sophisticated policies include Maximize Resource Utilization / Proportional Shares / Runtime Quotas / Access Restrictions / Interleaving / Priority Ranking
Web-UI driven policy configuration
Workload affiliation based decision making
Pluggable support for any Kubernetes distribution
On-the-fly policy re-configuration
Main points:
Machine Learning is increasingly impacting all of us, and we may seek to leverage existing or deploy enhanced infrastructures to address the demand
Containerization can sensibly play a role in this introduction, and containerizing Spark apps or services serves as a compelling use case
Container clusters are rapidly becoming enterprise grade – and Navops Command delivers workload placement and policy management capabilities that inevitably arise as adoption progresses
In addition to providing a converged platform for mixed workloads (legacy, non-containerized plus containerized), container clusters present the ideal opportunity to systematically introduce micro services based architectures – from refactoring to net-new implementations
CTA: We believe we have unique offerings to assist you in efficiently and effectively making this journey – into containerized clusters. Please visit our poster to learn more about converged platforms for Machine Learning – platforms that are enterprise ready through the introduction of Navops Command for Kubernetes container clusters – and drop by our table and posters in the exhibits area