Primatics Financial - Parallel, High Throughput Risk Calculations On The Cloud

•Download as PPT, PDF•

1 like•1,070 views

Technology Economy & Finance

Evolv ™ Risk: Cloud Computing Use Case for Java One 2009 Nati Shalom CTO GigaSpaces Natishalom.typepad.com Twitter.com/natishalom Francis de la Cruz Manager , Argyn Kuketayev Senior Consultant, Primatics Financial

Use Case Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Business Case ,[object Object],[object Object],[object Object]

Primatics’ Story in Cloud Computing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Business Needs: Accounting-Cashflows Risk Analytics Platform Stochastic (Monte Carlo) simulation: for each loan run 100 paths (360 months each) INPUT Loan Portfolio ( 100MB): 100k Loans x 1 KB data Forecast Scenarios (100MB): 1MB per simulation path Market History (<1MB) Assumptions, Setting, Dials (<1KB) OUTPUT Cashflows (3GB): 100k Loans x 360 months x 10 Doubles

Business Needs: Scale Many jobs by the same user Many users in a firm Many firms More loans, more simulations, longer time horizon Risk Analytics Platform

Overarching Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object]

Evolv Risk 1.5 vs 2.0 Software Stack GIGASPACES Prior Grid Framework J2EE Platform Loans & securities Loans Web-Interface SSL Reporting Warehouse Model API Scenarios Loss & Valuation Securities Custom Templates Validation Pluggable Client models Model Validation & statistics Cohorts HPI rates Interest rates Market rates Provisioning Monitoring Job Scheduling Failover / Recovery Load Balancing AWS Billing Registration Management Loss Forecasts Valuations Discount rates Node Management OLAP Reports Auto Throttling SLA Space Based Architecture Scaling Manual Process Model Validation & statistics Provisioning Monitoring Billing AWS Registration AWS management Not In Architecture Auto Throttling Space Based Architecture SLA Node Management * Broker (MQ) Sun JMS Discovery Persistence Manager J2EE Platform Inbuilt models

Performance, Scalability, Data Security, Data Integrity

Performance, Scalability, Data Security, Data Integrity Primatics Gateway AWS Provisioning

Lessons Learned : Cloud Integration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Lessons Learned : Data and Processing ,[object Object],[object Object],[object Object],[object Object]

Lessons Learned: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Real-Time Geospatial Intelligence at Scale

Real-Time Geospatial Intelligence at Scale

Real-Time Geospatial Intelligence at Scale

Real-Time Analytics with MemSQL and Spark

Real-Time Analytics with MemSQL and Spark

Real-Time Analytics with MemSQL and Spark

MemSQL - The Real-time Analytics Platform

MemSQL - The Real-time Analytics Platform

MemSQL - The Real-time Analytics Platform

Driving the On-Demand Economy with Spark and Predictive Analytics

Driving the On-Demand Economy with Spark and Predictive Analytics

Driving the On-Demand Economy with Spark and Predictive Analytics

DoorDash is a 3-sided marketplace that consists of Merchants, Consumers, and Dashers. As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and the ranking of the large volume of search queries. The prediction service built to meet above use cases now supports many dozens of models spanning different Machine Learning algorithms such as gradient boosting, neural networks and rule-based. The service supports greater than 10 billion predictions every day with a peak hit rate of above 1 million per second. In this session, we will share our journey of building and scaling our Machine Learning platform and particularly the prediction service, the various optimizations experimented, lessons learned, technical decisions and tradeoffs made. We will also share how we measure success and how we set goals for the future. Finally, we will end by highlighting the challenges ahead of us in extending our Machine Learning platform to support the Data Scientist community and a wider set use cases at DoorDash.

Scaling Online ML Predictions At DoorDash

Scaling Online ML Predictions At DoorDash

Scaling Online ML Predictions At DoorDash

MemSQL

Serverless Computing with Azure Functions

Serverless Computing with Azure Functions

Serverless Computing with Azure Functions

Alejandro Campos Magencio

Confluent x imply: Build the last mile to value for data streaming applications

Confluent x imply: Build the last mile to value for data streaming applications

Confluent x imply: Build the last mile to value for data streaming applications

In the on-demand economy real-time analytics is both a necessity and a competitive advantage. The next evolution in the on-demand economy is in predictive analytics fueled by live streams of data—in effect knowing what customers want before they do. This session will feature technical examples of real-time pipelines, machine learning, and custom dashboards as well as off-the-shelf dashboards with Tableau.

CTO View: Driving the On-Demand Economy with Predictive Analytics

CTO View: Driving the On-Demand Economy with Predictive Analytics

CTO View: Driving the On-Demand Economy with Predictive Analytics

Azure Machine Learning and Data Journeys

Azure Machine Learning and Data Journeys

Azure Machine Learning and Data Journeys

Debugging and interacting with production applications

Debugging and interacting with production applications

Debugging and interacting with production applications

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Intro to Delta Lake

Intro to Delta Lake

Intro to Delta Lake

Combinación de logs, métricas y rastreos para observabilidad unificada

Combinación de logs, métricas y rastreos para observabilidad unificada

Combinación de logs, métricas y rastreos para observabilidad unificada

Disrupting Risk Management through Emerging Technologies

Disrupting Risk Management through Emerging Technologies

Disrupting Risk Management through Emerging Technologies

Transforming data into actionable insights

Transforming data into actionable insights

Transforming data into actionable insights

Elastic Stack: Using data for insight and action

Elastic Stack: Using data for insight and action

Elastic Stack: Using data for insight and action

Real-Time Analytics with Confluent and MemSQL

Real-Time Analytics with Confluent and MemSQL

Real-Time Analytics with Confluent and MemSQL

Society depends on reliable utility services to ensure the health and safety of our communities. Electrical grid failures have impact and consequences that can range from daily inconveniences to catastrophic events. Ensuring grid reliability means that data is fully leveraged to understand and forecast demand, predict and mitigate unplanned interruptions to power supply, and efficiently restore power when needed. Neudesic, a Systems Integrator, and DTE Energy, a large electric and natural gas utility serving 2.2 million customers in southeast Michigan, partnered to use large IoT datasets to identify the sources and causes of reliability issues across DTE’s power distribution network. In this session, we will demonstrate how we ingest hundreds of millions of quality measures each day from DTE’s network of smart electric meters. This data is then further processed in Databricks to detect anomalies, apply graph analytics and spatially cluster these anomalies into “hot spots”. Engineers and Work Management Experts use a dashboard to explore, plan and prioritize diverse actions to remediate the hot spots. This allows DTE to prioritize work orders and dispatch crews based on impact to grid reliability. Because of this and other efforts, DTE has improved reliability by 25% year over year. We will demonstrate our notebooks and machine learning models along with our dashboard. We will also discuss Spark streaming, Pandas UDF’s, anomaly detection and DBSCAN clustering. By the end of our presentation, you should understand our approach to infer hidden insights from our IoT data, and potentially apply similar techniques to your own data.

Improving Power Grid Reliability Using IoT Analytics

Improving Power Grid Reliability Using IoT Analytics

Improving Power Grid Reliability Using IoT Analytics

What's hot (20)

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

Real-Time Geospatial Intelligence at Scale

Real-Time Geospatial Intelligence at Scale

Real-Time Geospatial Intelligence at Scale

Real-Time Analytics with MemSQL and Spark

Real-Time Analytics with MemSQL and Spark

Real-Time Analytics with MemSQL and Spark

MemSQL - The Real-time Analytics Platform

MemSQL - The Real-time Analytics Platform

MemSQL - The Real-time Analytics Platform

Driving the On-Demand Economy with Spark and Predictive Analytics

Driving the On-Demand Economy with Spark and Predictive Analytics

Driving the On-Demand Economy with Spark and Predictive Analytics

Scaling Online ML Predictions At DoorDash

Scaling Online ML Predictions At DoorDash

Scaling Online ML Predictions At DoorDash

MemSQL

Serverless Computing with Azure Functions

Serverless Computing with Azure Functions

Serverless Computing with Azure Functions

Confluent x imply: Build the last mile to value for data streaming applications

Confluent x imply: Build the last mile to value for data streaming applications

Confluent x imply: Build the last mile to value for data streaming applications

CTO View: Driving the On-Demand Economy with Predictive Analytics

CTO View: Driving the On-Demand Economy with Predictive Analytics

CTO View: Driving the On-Demand Economy with Predictive Analytics

Azure Machine Learning and Data Journeys

Azure Machine Learning and Data Journeys

Azure Machine Learning and Data Journeys

Debugging and interacting with production applications

Debugging and interacting with production applications

Debugging and interacting with production applications

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Tapjoy: Building a Real-Time Data Science Service for Mobile Advertising

Intro to Delta Lake

Intro to Delta Lake

Intro to Delta Lake

Combinación de logs, métricas y rastreos para observabilidad unificada

Combinación de logs, métricas y rastreos para observabilidad unificada

Combinación de logs, métricas y rastreos para observabilidad unificada

Disrupting Risk Management through Emerging Technologies

Disrupting Risk Management through Emerging Technologies

Disrupting Risk Management through Emerging Technologies

Transforming data into actionable insights

Transforming data into actionable insights

Transforming data into actionable insights

Elastic Stack: Using data for insight and action

Elastic Stack: Using data for insight and action

Elastic Stack: Using data for insight and action

Real-Time Analytics with Confluent and MemSQL

Real-Time Analytics with Confluent and MemSQL

Real-Time Analytics with Confluent and MemSQL

Improving Power Grid Reliability Using IoT Analytics

Improving Power Grid Reliability Using IoT Analytics

Improving Power Grid Reliability Using IoT Analytics

Similar to Primatics Financial - Parallel, High Throughput Risk Calculations On The Cloud

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Getting Ready For The Cloud

Giga Spaces Getting Ready For The Cloud

Giga Spaces Getting Ready For The Cloud

Mr Nati Shalom, Founder and CTO of GigaSpaces Nati is responsible for defining the technology roadmap and the direction of GigaSpaces products as they relate to standards adaptations, architecture, and product design. He has more than 10 years of experience with distributed technology and architecture namely CORBA, Jini, J2EE, Grid and SOA. He has been working for the past ten years with some of the leading Israeli companies, such as ECI, Comverse, BMC, Elisra, Rafael, and Amdocs. He has led the development of the first Reverse BID exchange in the Israeli Yellow Pages. He previously worked with IONA, and was responsible for the penetration of their products and technology, to most of the leading ISV's in Israel. As the Head of the Israeli Grid consortium, Mr. Shalom is recognized as a software visionary and industry leader, he is a frequent presenter at industry conferences and is actively involved in evangelizing Space Based Architecture, Data Grid patterns, and Cloud Computing. ______________________________________________________________________________________________________________ Topic - Getting Ready for the Cloud – Technology In this session Nati will describe what is the latest developments in the industry on cloud computing, and where he feels this will be going. He will also share his experience on how to design and deploy enterprise applications in a cloud/grid computing platform, what to take into account while developing or deploying applications on the cloud, and demonstrate how to transition applications to run on the Cloud without needing to completely re-architect them. Standard Application Servers as we've known them only partially address enterprises' needs for scalability. As a result, a new class of application servers has emerged, focused on massive scalability. In this session, we will explore some of the common characteristics of these servers while looking at how to migrate an existing Java EE web app to a scale-out application server, relatively seamlessly. Included is a 10-minute demo on turning an existing tier-based application into a tierless scaled out application running on the Amazon EC2 Cloud. In this live demo session, we will also use the cloud-based environment to demonstrate how you can add dynamic scaling, self healing and improved performance with almost no changes to your code.

GigaSpaces - Getting Ready For The Cloud

GigaSpaces - Getting Ready For The Cloud

GigaSpaces - Getting Ready For The Cloud

Windowsazureplatform Overviewlatest

Windowsazureplatform Overviewlatest

Windowsazureplatform Overviewlatest

5 Years Of Building SaaS On AWS

5 Years Of Building SaaS On AWS

5 Years Of Building SaaS On AWS

Christian Beedgen

This presentation provides a case study on how to overcome some of the challenges involved in deploying a SaaS application on the cloud using GigaSpaces. In this specific case were referring to a risk analysis application (Primatics) and describe how we've dealt with multi tenancy, high availability, scalability and how we were able to optimize the architecture for reduced cost. The presentation was given during the cloud connect 2010 conference.

Deploying SaaS Application on the Cloud - Case Study

Deploying SaaS Application on the Cloud - Case Study

Deploying SaaS Application on the Cloud - Case Study

An unstated assumption is that clouds are scalable. But are they? Stick thousands upon thousands of machines together and there are a lot of potential bottlenecks just waiting to choke off your scalability supply. And if the cloud is scalable what are the chances that your application is really linearly scalable? At 10 machines all may be well. Even at 50 machines the seas look calm. But at 100, 200, or 500 machines all hell might break loose. How do you know? You know through real life testing. These kinds of tests are brutally hard and complicated. who wants to do all the incredibly precise and difficult work of producing cloud scalability tests? GridDynamics has stepped up to the challenge and has just released their Cloud Performance Reports.

Using Grid Technologies in the Cloud for High Scalability

Using Grid Technologies in the Cloud for High Scalability

Using Grid Technologies in the Cloud for High Scalability

Hostingcon 2010 Cloud Presentation

Hostingcon 2010 Cloud Presentation

Hostingcon 2010 Cloud Presentation

Keynote at Scale By The Bay 2020. Cloud service developers need to handle massive scale workloads from thousands of customers with no downtime or regressions. In this talk, I’ll present our experience building a very large-scale cloud service at Databricks, which provides a data and ML platform service used by many of the largest enterprises in the world. Databricks manages millions of cloud VMs that process exabytes of data per day for interactive, streaming and batch production applications. This means that our control plane has to handle a wide range of workload patterns and cloud issues such as outages. We will describe how we built our control plane for Databricks using Scala services and open source infrastructure such as Kubernetes, Envoy, and Prometheus, and various design patterns and engineering processes that we learned along the way. In addition, I’ll describe how we have adapted data analytics systems themselves to improve reliability and manageability in the cloud, such as creating an ACID storage system that is as reliable as the underlying cloud object store (Delta Lake) and adding autoscaling and auto-shutdown features for Apache Spark.

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

RightScale Webinar: July 23, 2009 – Listen to a recorded live panel discussion moderated by Dave Nielsen, founder of CloudCamp and Principal Consultant at Platform D. Hear from leading experts in cloud computing to gain real-world insight into how their customers are using cloud computing and achieving new levels of backup, recovery and reliability. Learn how you can migrate your apps and data to the cloud in a way that's affordable and reliable. Watch video at http://vimeo.com/rightscale/transitioning-to-the-cloud.

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Azure Overview Csco

Azure Overview Csco

Azure Overview Csco

Giga spaces value prop - afas - cloud practices

Giga spaces value prop - afas - cloud practices

Giga spaces value prop - afas - cloud practices

Tricode (part of Dept)

Understand the core concepts of “Cloud Computing” and how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users. Whether you are an enterprise looking for IT innovation, agility and resiliency or small and medium business who wants to accelerate growth without a big upfront investment in cash or time for technology, the AWS Cloud provides a complete set of services at zero upfront costs which are available with a few clicks and within minutes.

Introduction to Cloud Computing with Amazon Web Services

Introduction to Cloud Computing with Amazon Web Services

Introduction to Cloud Computing with Amazon Web Services

Amazon Web Services

Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management. The slides were from a webinar presented January 24, 2017. The audience learned: - How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand - Best practices for efficient use of cloud compute and storage resources - Overcoming the need for file systems within a hybrid cloud environment - Understand how to eliminate latency between cloud and data center architectures - Learn how to best manage simulation, analytics, and big data workloads in dynamic environments - Look at market dynamics drawing companies to new storage models over the next several years Presenters communicated a foundation to build infrastructure to support ongoing demand growth.

Solving enterprise challenges through scale out storage & big compute final

Solving enterprise challenges through scale out storage & big compute final

Solving enterprise challenges through scale out storage & big compute final

RightScale Webinar: August 11, 2009 - Watch this webinar to see a hands-on demonstration of WaveMaker Visual Ajax Studio and Rapid Deployment Framework to illustrate how easy it is to build your app in Wavemaker. We demonstrate the one-button push from Wavemaker to deploying your application on the cloud with the RightScale Cloud Management Platform. From there we show you how easy it is to manage, automate and scale your application running on the cloud.

Build & Deploy Scalable Cloud Applications in Record Time

Build & Deploy Scalable Cloud Applications in Record Time

Build & Deploy Scalable Cloud Applications in Record Time

Cloud computing pioneers - remarkable examples 2010-11-05

Cloud computing pioneers - remarkable examples 2010-11-05

Cloud computing pioneers - remarkable examples 2010-11-05

Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

The important bits of cloud computing

The important bits of cloud computing

The important bits of cloud computing

Carsonified Team

Keynote by Matei Zaharia at SOCC 2019 Abstract: The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software, which has not been heavily studied in research. I will explain some of these challenges based on my experience at Databricks, a startup that provides a data analytics platform as a service on AWS and Azure. Databricks manages millions of VMs per day to run data engineering and machine learning workloads using Apache Spark, TensorFlow, Python and other software for thousands of customers. Two main challenges arise in this context: (1) building a reliable, scalable control plane that can manage thousands of customers at once and (2) adapting the data processing software itself (e.g. Apache Spark) for an elastic cloud environment (for instance, autoscaling instead of assuming static clusters). These challenges are especially significant for data analytics workloads whose users constantly push boundaries in terms of scale (e.g. number of VMs used, data size, metadata size, number of concurrent users, etc). I’ll describe some of the common challenges that our new services face and some of the main ways that Databricks has extended and modified open source analytics software for the cloud environment (e.g., designing an autoscaling engine for Apache Spark and creating a transactional storage layer on top of S3 in the Delta Lake open source project). Bio: Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly on datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow open source machine learning platform at Databricks and is a PI in the DAWN Lab focusing on systems for ML at Stanford. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).

Lessons from Large-Scale Cloud Software at Databricks

Lessons from Large-Scale Cloud Software at Databricks

Lessons from Large-Scale Cloud Software at Databricks

Excellent slides on the new z13s announced on 16th Feb 2016

Excellent slides on the new z13s announced on 16th Feb 2016

Excellent slides on the new z13s announced on 16th Feb 2016

Similar to Primatics Financial - Parallel, High Throughput Risk Calculations On The Cloud (20)

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Alternative To GAE_JavaOne 09

Giga Spaces Getting Ready For The Cloud

Giga Spaces Getting Ready For The Cloud

Giga Spaces Getting Ready For The Cloud

GigaSpaces - Getting Ready For The Cloud

GigaSpaces - Getting Ready For The Cloud

GigaSpaces - Getting Ready For The Cloud

Windowsazureplatform Overviewlatest

Windowsazureplatform Overviewlatest

Windowsazureplatform Overviewlatest

5 Years Of Building SaaS On AWS

5 Years Of Building SaaS On AWS

5 Years Of Building SaaS On AWS

Deploying SaaS Application on the Cloud - Case Study

Deploying SaaS Application on the Cloud - Case Study

Deploying SaaS Application on the Cloud - Case Study

Using Grid Technologies in the Cloud for High Scalability

Using Grid Technologies in the Cloud for High Scalability

Using Grid Technologies in the Cloud for High Scalability

Hostingcon 2010 Cloud Presentation

Hostingcon 2010 Cloud Presentation

Hostingcon 2010 Cloud Presentation

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

Scaling Databricks to Run Data and ML Workloads on Millions of VMs

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Transitioning to the Cloud: Implications for Reliability, Redundancy & Recove...

Azure Overview Csco

Azure Overview Csco

Azure Overview Csco

Giga spaces value prop - afas - cloud practices

Giga spaces value prop - afas - cloud practices

Giga spaces value prop - afas - cloud practices

Introduction to Cloud Computing with Amazon Web Services

Introduction to Cloud Computing with Amazon Web Services

Introduction to Cloud Computing with Amazon Web Services

Solving enterprise challenges through scale out storage & big compute final

Solving enterprise challenges through scale out storage & big compute final

Solving enterprise challenges through scale out storage & big compute final

Build & Deploy Scalable Cloud Applications in Record Time

Build & Deploy Scalable Cloud Applications in Record Time

Build & Deploy Scalable Cloud Applications in Record Time

Cloud computing pioneers - remarkable examples 2010-11-05

Cloud computing pioneers - remarkable examples 2010-11-05

Cloud computing pioneers - remarkable examples 2010-11-05

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...

The important bits of cloud computing

The important bits of cloud computing

The important bits of cloud computing

Lessons from Large-Scale Cloud Software at Databricks

Lessons from Large-Scale Cloud Software at Databricks

Lessons from Large-Scale Cloud Software at Databricks

Excellent slides on the new z13s announced on 16th Feb 2016

Excellent slides on the new z13s announced on 16th Feb 2016

Excellent slides on the new z13s announced on 16th Feb 2016

Recently uploaded

New customer? New industry? New cloud? New team? A lot to handle! How to ensure the success of the project? Start it well! I've created the 3 areas of focus at the beginning of the project that helped me in multiple roles (BA, PO, and Consultant). Learn from real-world experiences and discover how these insights can empower you to deliver unparalleled value to your customers right from the project's start.

Powerful Start- the Key to Project Success, Barbara Laskowska

Powerful Start- the Key to Project Success, Barbara Laskowska

Powerful Start- the Key to Project Success, Barbara Laskowska

Ever caught yourself nodding along when someone mentions "delivering value" in Agile, but secretly wondering what the heck they actually mean? You're not alone! Join us for an eye-opening session where we'll strip away the buzzwords and dive into the heart of Agile—value delivery. But what is "value"? Is it a mythical unicorn in the world of software development, or is there more to this overused term? This isn't going to be a sit-and-get lecture. We're talking about a face-to-face, interactive meetup where YOU play a crucial role. Come along to: Define It: What does "value" really mean? We’ll build a definition that’s not just words, but a compass for your Agile journey. Contextualise It: Discover what value means specifically to you, your team, your company, and your industry. Because one size does not fit all. Deliver It: Share strategies and gather new ones for uncovering and delivering true value—no more shooting in the dark!

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

This talk offers actionable insights at an executive level for enhancing productivity and refining your portfolio management approach to propel your organization to greater heights. Key Points Covered: 1. Experience Transformation: - The core challenge remains consistent across organizations: converting budget into user-centric designs. - Strategies for deploying design resources effectively in both startups and large enterprises. 2. Strategic Frameworks: - Introduction to the "Ziggurat of Impact" model, detailing layers from basic system interactions to comprehensive customer experiences. - Practical insights on creating frameworks that scale with organizational complexity. 3. Organizational Impact: - Real-world examples of navigating design in large settings, focusing on the synthesis of consumer products and customer experiences. - Emphasis on the importance of designing systems that directly influence customer interactions. 4. Design Execution: - Detailed walkthrough of organizational layers affecting design execution, from touchpoints and customer activities to shared capabilities. - How to ensure design influences both the micro and macro aspects of customer interactions. 5. Measurement and Adaptation: - Techniques for measuring the impact of design decisions and adapting strategies based on data-driven insights. - The critical role of continuous improvement and feedback in refining customer experiences.

Structuring Teams and Portfolios for Success

Structuring Teams and Portfolios for Success

Structuring Teams and Portfolios for Success

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

We're living the AI revolution and Salesforce is adapting and bring new value to their customers. Einstein products are evolving rapidly and navigating their limitations, language support, and use cases can be challenging. Let's make review of what Einstein product are available currently, what are the capabilities and what can be used for in CEE region and how Rossie.ai can help to learn Salesforce speak Czech. We will explore the Einstein roadmap and I will make a short live demo (based on your vote) of some Einstein feature.

AI revolution and Salesforce, Jiří Karpíšek

AI revolution and Salesforce, Jiří Karpíšek

AI revolution and Salesforce, Jiří Karpíšek

Webinar Recording: https://www.panagenda.com/webinars/alles-neu-macht-der-mai-wir-durchleuchten-den-verbesserten-notes-eigenschaftendialog/ Haben Sie sich schon einmal über den zu kleinen Eigenschaftendialog in Notes geärgert? Mussten Sie einen Agenten oder eine Aktion erstellen, um schnell mal ein Feld zu ändern? Haben Sie jedes mal endlos nach dem zu vergleichenden Feld gesucht, nachdem Sie ein neues Dokument ausgewählt haben? Wollten Sie das verdammte Ding einfach nur größer machen? Zum Glück gibt es dafür eine Lösung – und sie ist wahrscheinlich bereits installiert! Mit dem kostenlosen panagenda Document Properties (Pro) erhalten Sie den Eigenschaftendialog, den Sie schon immer haben wollten. Größer, anpassbar, und im Volltext durchsuchbar. Sehen Sie mehrere Dokumente gleichzeitig oder vergleichen Sie mit einem Diff-Viewer. Ändern Sie beliebige Felder und haben Sie endlich eine einfache Möglichkeit, Profildokumente für alle Benutzer zu verwalten. Entdecken Sie mit HCL Ambassador Marc Thomas, wie Document Properties Ihre Arbeit vereinfachen und Sie bei der täglichen Verwendung von Domino-Anwendungen unterstützen kann – im Client oder im Designer. Sie werden es nicht bereuen! Für Sie in diesem Webinar - Was Document Properties ist, welche Editionen es gibt und wo es in Notes und Domino Designer zu finden ist - Wie Sie nach einem beliebigen Feld suchen und es bearbeiten, Dokumente vergleichen oder alle Daten per CSV exportieren können - Suchen, Bearbeiten und auch Löschen von Profildokumenten - Welche Konfigurationseinstellungen verfügbar sind, um Funktionen anzupassen - Wie Ihre Endbenutzer davon profitieren - Sehen Sie alles in einer Live-Demo

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

WebAssembly is Key to Better LLM Performance

WebAssembly is Key to Better LLM Performance

WebAssembly is Key to Better LLM Performance

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

Unlock the mysteries of successful Salesforce interviews in this insightful session hosted by Hugo Rosario (Salesforce Customer), a seasoned hiring manager that leads the Salesforce Department of multinational company with over 100 interviews under their belt. Step into the manager's chair and gain exclusive behind-the-scenes insights into what makes a Salesforce consultant stand out during the interview process. From deciphering the unspoken cues to mastering key strategies, we'll explore the intricacies of the interview process and provide practical tips for consultants looking to not only pass interviews but also thrive in their roles. Whether you're a seasoned professional or just starting your Salesforce journey, this session is your backstage pass to the secrets that hiring managers wish you knew.

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Intro in Product Management - Коротко про професію продакт менеджера

Intro in Product Management - Коротко про професію продакт менеджера

Intro in Product Management - Коротко про професію продакт менеджера

Using IESVE for Room Loads Analysis - UK & Ireland

Using IESVE for Room Loads Analysis - UK & Ireland

Using IESVE for Room Loads Analysis - UK & Ireland

Intrigued by why some of the world's largest companies (Netflix, Google, Cisco, Twitter, Uber etc) are using gRPC? In this demo based talk we delve into the world of gRPC in .Net, what it does and why we should use it. We compare the interface with both Rest and graphQL. We will show you how to implement grpc server-side in .net and in the web. Finally, I will show you how the tooling helps you deliver powerful interfaces and interact with them quickly and simply.

Demystifying gRPC in .Net by John Staveley

Demystifying gRPC in .Net by John Staveley

Demystifying gRPC in .Net by John Staveley

AI presentation and introduction - Retrieval Augmented Generation RAG 101

AI presentation and introduction - Retrieval Augmented Generation RAG 101

AI presentation and introduction - Retrieval Augmented Generation RAG 101

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “Enterprise Knowledge Graphs: The Importance of Semantics” on May 9, 2024, at the annual Data Summit in Boston. In her presentation, Hedden describes the components of an enterprise knowledge graph and provides further insight into the semantic layer – or knowledge model – component, which includes an ontology and controlled vocabularies, such as taxonomies, for controlled metadata. While data experts tend to focus on the graph database components (RDF triple store or a label property graph), Hedden emphasizes they should not overlook the importance of the semantic layer.

Enterprise Knowledge Graphs - Data Summit 2024

Enterprise Knowledge Graphs - Data Summit 2024

Enterprise Knowledge Graphs - Data Summit 2024

Enterprise Knowledge

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Oauth 2.0 Introduction and Flows with MuleSoft

Oauth 2.0 Introduction and Flows with MuleSoft

Oauth 2.0 Introduction and Flows with MuleSoft

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

marcuskenyatta275

Recently uploaded (20)

Powerful Start- the Key to Project Success, Barbara Laskowska

Powerful Start- the Key to Project Success, Barbara Laskowska

Powerful Start- the Key to Project Success, Barbara Laskowska

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

Structuring Teams and Portfolios for Success

Structuring Teams and Portfolios for Success

Structuring Teams and Portfolios for Success

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

AI revolution and Salesforce, Jiří Karpíšek

AI revolution and Salesforce, Jiří Karpíšek

AI revolution and Salesforce, Jiří Karpíšek

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...

WebAssembly is Key to Better LLM Performance

WebAssembly is Key to Better LLM Performance

WebAssembly is Key to Better LLM Performance

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...

Intro in Product Management - Коротко про професію продакт менеджера

Intro in Product Management - Коротко про професію продакт менеджера

Intro in Product Management - Коротко про професію продакт менеджера

Using IESVE for Room Loads Analysis - UK & Ireland

Using IESVE for Room Loads Analysis - UK & Ireland

Using IESVE for Room Loads Analysis - UK & Ireland

Demystifying gRPC in .Net by John Staveley

Demystifying gRPC in .Net by John Staveley

Demystifying gRPC in .Net by John Staveley

AI presentation and introduction - Retrieval Augmented Generation RAG 101

AI presentation and introduction - Retrieval Augmented Generation RAG 101

AI presentation and introduction - Retrieval Augmented Generation RAG 101

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...

Enterprise Knowledge Graphs - Data Summit 2024

Enterprise Knowledge Graphs - Data Summit 2024

Enterprise Knowledge Graphs - Data Summit 2024

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Oauth 2.0 Introduction and Flows with MuleSoft

Oauth 2.0 Introduction and Flows with MuleSoft

Oauth 2.0 Introduction and Flows with MuleSoft

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...

Primatics Financial - Parallel, High Throughput Risk Calculations On The Cloud

1. Evolv ™ Risk: Cloud Computing Use Case for Java One 2009 Nati Shalom CTO GigaSpaces Natishalom.typepad.com Twitter.com/natishalom Francis de la Cruz Manager , Argyn Kuketayev Senior Consultant, Primatics Financial

2.

3.

4.

5. Business Needs: Accounting-Cashflows Risk Analytics Platform Stochastic (Monte Carlo) simulation: for each loan run 100 paths (360 months each) INPUT Loan Portfolio ( 100MB): 100k Loans x 1 KB data Forecast Scenarios (100MB): 1MB per simulation path Market History (<1MB) Assumptions, Setting, Dials (<1KB) OUTPUT Cashflows (3GB): 100k Loans x 360 months x 10 Doubles

6. Business Needs: Scale Many jobs by the same user Many users in a firm Many firms More loans, more simulations, longer time horizon Risk Analytics Platform

7.

8. Evolv Risk 1.5 vs 2.0 Software Stack GIGASPACES Prior Grid Framework J2EE Platform Loans & securities Loans Web-Interface SSL Reporting Warehouse Model API Scenarios Loss & Valuation Securities Custom Templates Validation Pluggable Client models Model Validation & statistics Cohorts HPI rates Interest rates Market rates Provisioning Monitoring Job Scheduling Failover / Recovery Load Balancing AWS Billing Registration Management Loss Forecasts Valuations Discount rates Node Management OLAP Reports Auto Throttling SLA Space Based Architecture Scaling Manual Process Model Validation & statistics Provisioning Monitoring Billing AWS Registration AWS management Not In Architecture Auto Throttling Space Based Architecture SLA Node Management * Broker (MQ) Sun JMS Discovery Persistence Manager J2EE Platform Inbuilt models

9. Performance, Scalability, Data Security, Data Integrity

10. Performance, Scalability, Data Security, Data Integrity Primatics Gateway AWS Provisioning

11.

12.

13.