Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
Six steps to leveraging location for the Canadian insurance industryDMTI Spatial
The key to minimizing your risk and improving your profitability lies in leveraging location throughout the value chain. There are 6 steps to leveraging high precision location throughout your various business processes and systems to reduce risk, increase profitability and improve customer, agent, broker and underwriter satisfaction
Productionising Machine Learning to automate the enterprise. Conference research question: How can you pin-point which core business processes to transform with increased automation and streamline daily workflows to boost in house efficiencies?
Six steps to leveraging location for the Canadian insurance industryDMTI Spatial
The key to minimizing your risk and improving your profitability lies in leveraging location throughout the value chain. There are 6 steps to leveraging high precision location throughout your various business processes and systems to reduce risk, increase profitability and improve customer, agent, broker and underwriter satisfaction
Data reply sneak peek: real time decision enginesconfluent
Events happen constantly in every business: a purchase in an online shop, a credit limit is hit, the mobile internet plan has been exhausted, users interact with a website. Events rule the business world. So why would you react to them hours or days later? Real-Time Decision Engines enable a variety of use cases, driving new products, increasing user experience, reducing costs and risks by reacting instantly to business events.
From personalized instantaneous marketing campaigns to reacting to user interactions, Real-Time is the key to open up a world of use cases that batch and scheduled processing cannot efficiently satisfy. In this talk, we are going to show some example use cases that Data Reply developed for some of its customers and how Real-Time Decision Engines had an impact on their businesses.
Cognitive Procurement Masterclass with IBM - SID 51774SAP Ariba
Understand how SAP Ariba solutions enabled by IBM Watson are shaping the future of cognitive procurement. In this session, we go deep into cognitive procurement use cases and share how you can unlock value in your business. Learn how your organization can transform from seeking process standardization and simplification to building automation and intelligence in its source-to-settle platform. Find out how you can achieve greater efficiency and savings and better risk management.
With rising business challenges in the aftermarket service areas, it becomes imperative for manufacturers to gain actionable intelligence across the warranty management life cycle.
Join Revolution Analytics and Tech Mahindra to hear how to reduce the information visibility gap:
• Identify statistically significant business drivers
• Forecast warranty costs and claims
• Improve Customer Satisfaction
Nurturing Digital Twins: How to Build Virtual Instances of Physical Assets to...Cognizant
To embark on the digital twin jounrey, assess your readiness, define and communicate a vision, set common data management rules and build in flexibility for intelligence.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
Project Deliverable 4 Analytics, Interfaces, and Cloud Technolo.docxwkyra78
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Main screen
Overview | Export data | Tools | Realtime | Logout
Current month
Last
Month
Trends
c
Top selling products
Low selling products
Overview
Realtime information
Overview | Export data | Tools | Realtime | Logout
Unique ID
Activity
Result
Overview | Export data | Tools | Realtime | Logout
Reporting tools
Statistical tools
Trends
Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Introduction
Business Analytics means the practice of iterative and methodological examination of a business’s data with a special emphasis on statistic making. Business Analytics can further help businesses automate and optimize their business processes. Companies in which data plays a pivotal role, treats its data as a corporate assets and leverages it for gaining competitive advantage. A successful business analytics would typically depend on data quality, highly skillful and experienced professionals who understand the technologies, knows how to work with it and also understands the organizations processes in depth. Apart from this, the organization should have a capable infrastructure to support the operations of business analytics.
Usage of Business Analysis is done for the following purposes:
· Exploration of data so as to find patterns and trends
· Identifying relationships in key data variables for forecasting. For instance next probable purchase by the customer
· Drilling down to the results to find out why a particular incident took place. This approach is done by performing statistical analysis and quantitative analysis with business analytical tools
· Predicting future results by employing predictive modeling and predictive analytics
· Testing previous decisions using A/B and Multivariate testing
· Assisting business in decision making such as figuring out the amount of discount to be given for a new customer
Post identifying of business goal, an analysis methodology needs to be selected and the data is acquired to support the analysis. This data acquisition normally involves extracting data from systems that may be spread throughout different locations an ...
Robotic process automation powers digital transformation in insurance industryArtivatic.ai
The era of robotic process automation (RPA) coupled with deep learning is here. From back-office functions to customer solutions, it has effectively turned processes around on their heads. Leading banks, hedge funds, and asset managers have successfully leveraged RPA tools not only to streamline standard processes but also to save money significantly.
BRIDGEi2i has frameworks to establish Analytics CoE for Supply Chain functions within organizations. Demand planning solution of BRIDGEi2i aims at using advanced statistical forecasting coupled with real-time decision engines models for demand planning, inventory optimization.
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHortonworks
Whether you are an insurer, reinsurer, broker or insurance service provider; everything you do is based on analytics. From underwriting to claims to agency and marketing, the smartest and most streamlined business operations at insurance companies are driven by advanced and intelligent analytics. But is your data ready? Are you an “Analytics Ready” insurer? Great analytics starts with great data management. Join us as industry experts from Informatica and Hortonworks share industry trends and best practices to show you how to become an “Analytics Ready” insurer.
Insurance - Open Source Analytics Dashboards for Real Time Business OverviewEuro IT Group
Check this Slide Deck to understand how open source analytics dashboards can support better strategic decisions. Such dashboards can be available in a matter of hours if data is available within your systems. If not, we can make it available.
“In today’s digital world, businesses that want to master the flow of information have to address three key challenges: the explosive growth in data volumes, the need to analyse those growing volumes in real-time, and the need to deliver the resulting insights to users...” ‘Insights Everywhere’ Intel White Paper
We believe digitization and automation are the means for institutions to drastically improve their compliance return on investment. Technology solutions like Risk Assessments, customer on boarding, cross-channel risk analysis, monitoring and screening, etc… should be looked at as part of the overall business plan and growth in order to achieve Strategic Compliance Planning.
Data reply sneak peek: real time decision enginesconfluent
Events happen constantly in every business: a purchase in an online shop, a credit limit is hit, the mobile internet plan has been exhausted, users interact with a website. Events rule the business world. So why would you react to them hours or days later? Real-Time Decision Engines enable a variety of use cases, driving new products, increasing user experience, reducing costs and risks by reacting instantly to business events.
From personalized instantaneous marketing campaigns to reacting to user interactions, Real-Time is the key to open up a world of use cases that batch and scheduled processing cannot efficiently satisfy. In this talk, we are going to show some example use cases that Data Reply developed for some of its customers and how Real-Time Decision Engines had an impact on their businesses.
Cognitive Procurement Masterclass with IBM - SID 51774SAP Ariba
Understand how SAP Ariba solutions enabled by IBM Watson are shaping the future of cognitive procurement. In this session, we go deep into cognitive procurement use cases and share how you can unlock value in your business. Learn how your organization can transform from seeking process standardization and simplification to building automation and intelligence in its source-to-settle platform. Find out how you can achieve greater efficiency and savings and better risk management.
With rising business challenges in the aftermarket service areas, it becomes imperative for manufacturers to gain actionable intelligence across the warranty management life cycle.
Join Revolution Analytics and Tech Mahindra to hear how to reduce the information visibility gap:
• Identify statistically significant business drivers
• Forecast warranty costs and claims
• Improve Customer Satisfaction
Nurturing Digital Twins: How to Build Virtual Instances of Physical Assets to...Cognizant
To embark on the digital twin jounrey, assess your readiness, define and communicate a vision, set common data management rules and build in flexibility for intelligence.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
Project Deliverable 4 Analytics, Interfaces, and Cloud Technolo.docxwkyra78
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Main screen
Overview | Export data | Tools | Realtime | Logout
Current month
Last
Month
Trends
c
Top selling products
Low selling products
Overview
Realtime information
Overview | Export data | Tools | Realtime | Logout
Unique ID
Activity
Result
Overview | Export data | Tools | Realtime | Logout
Reporting tools
Statistical tools
Trends
Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Introduction
Business Analytics means the practice of iterative and methodological examination of a business’s data with a special emphasis on statistic making. Business Analytics can further help businesses automate and optimize their business processes. Companies in which data plays a pivotal role, treats its data as a corporate assets and leverages it for gaining competitive advantage. A successful business analytics would typically depend on data quality, highly skillful and experienced professionals who understand the technologies, knows how to work with it and also understands the organizations processes in depth. Apart from this, the organization should have a capable infrastructure to support the operations of business analytics.
Usage of Business Analysis is done for the following purposes:
· Exploration of data so as to find patterns and trends
· Identifying relationships in key data variables for forecasting. For instance next probable purchase by the customer
· Drilling down to the results to find out why a particular incident took place. This approach is done by performing statistical analysis and quantitative analysis with business analytical tools
· Predicting future results by employing predictive modeling and predictive analytics
· Testing previous decisions using A/B and Multivariate testing
· Assisting business in decision making such as figuring out the amount of discount to be given for a new customer
Post identifying of business goal, an analysis methodology needs to be selected and the data is acquired to support the analysis. This data acquisition normally involves extracting data from systems that may be spread throughout different locations an ...
Robotic process automation powers digital transformation in insurance industryArtivatic.ai
The era of robotic process automation (RPA) coupled with deep learning is here. From back-office functions to customer solutions, it has effectively turned processes around on their heads. Leading banks, hedge funds, and asset managers have successfully leveraged RPA tools not only to streamline standard processes but also to save money significantly.
BRIDGEi2i has frameworks to establish Analytics CoE for Supply Chain functions within organizations. Demand planning solution of BRIDGEi2i aims at using advanced statistical forecasting coupled with real-time decision engines models for demand planning, inventory optimization.
How to Become an Analytics Ready Insurer - with Informatica and HortonworksHortonworks
Whether you are an insurer, reinsurer, broker or insurance service provider; everything you do is based on analytics. From underwriting to claims to agency and marketing, the smartest and most streamlined business operations at insurance companies are driven by advanced and intelligent analytics. But is your data ready? Are you an “Analytics Ready” insurer? Great analytics starts with great data management. Join us as industry experts from Informatica and Hortonworks share industry trends and best practices to show you how to become an “Analytics Ready” insurer.
Insurance - Open Source Analytics Dashboards for Real Time Business OverviewEuro IT Group
Check this Slide Deck to understand how open source analytics dashboards can support better strategic decisions. Such dashboards can be available in a matter of hours if data is available within your systems. If not, we can make it available.
“In today’s digital world, businesses that want to master the flow of information have to address three key challenges: the explosive growth in data volumes, the need to analyse those growing volumes in real-time, and the need to deliver the resulting insights to users...” ‘Insights Everywhere’ Intel White Paper
We believe digitization and automation are the means for institutions to drastically improve their compliance return on investment. Technology solutions like Risk Assessments, customer on boarding, cross-channel risk analysis, monitoring and screening, etc… should be looked at as part of the overall business plan and growth in order to achieve Strategic Compliance Planning.
NFT란.. 대체 불가능한 토큰이라는 의미로 블록체인기반의 고유 코드를 부여해서 디지털 자산의 원본임을 증명하는 기술입니다. 즉, 교환과 복제가 불가능하여 저마다 고유성과 희소성을 지니는 블록체인 기반의 토큰이어서 영상·그림·음악 등을 복제 불가능한 콘텐츠로 만들 수 있어 신종 디지털 자산으로 주목 받고 있습니다.
NFT Minting의 Minting이란 주조한다는 의미로 즉 앞서의 NFT의 최초 발행 시점에 코인을 지불하고 NFT를 발행하는 것을 의미합니다. 이러한 작업을 위해 Minting 플랫폼을 구축해야 하는데 이러한 구축 작업을 저희가 제공하고자 합니다.
저희는 현재 디지털 컨텐츠 제공자들(웹툰, 그림, 사진, 영화, 스포츠, 게임, 음악....)의 요구로 이러한 NFT Minting 사이트인 NFT 마켓 플레이스 웹사이트를 구축해 드리고 있습니다....또한, 저희 자체로도 디지털 컨텐츠 자산 보호 관련한 Minting 사이트를 구축해서 외부 디지털 컨텐츠 제공업체와 협업으로 NFT 비지니스도 진행하고 있습니다....
빅데이터 구축 및 술루션 가이드 주요 내용
- 고객 내부 빅데이터 프로젝트 진행시 단계별 진행 가이드
- 빅데이터 프로젝트 구축 타입
- 각 산업분야별로 빅데이터 엔진을 활용한 솔루션 구축 가이드
(딥 러닝 기법 기반의 분석 포함)
코세나(kosena), 이승훈 실장 admin@kosena.kr, kosena21@naver.com
010-9338-6400
스플렁크로 구축한 빅데이터 기반의 FDS 사례 자료입니다...
방대한 내부 데이터를 분석하여 실시간으로 통제하는 빅데이터 기반의 FDS(Fraud Detection System)으로 기존의 배치성이거나 특정 분야만 지원하는 FDS가 아니라 모든 금융 및 기업 내외부의 부정행위를 분석하고 모니터링합니다...
이제 진정한 FDS를 구축하실 수 있습니다...
코세나(kosena), 이승훈 실장 admin@kosena.kr, kosena21@naver.com
010-9338-6400
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-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
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
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5. v
H2O.ai Confidential
Cyber-threat detection
Objective
● Help information security teams reduce risk and improve their
security posture efficiently and effectively
● Automate threat detection across large numbers of devices,
attack vectors, and data silos
Outcome
● Continuous assessment of asset inventory to gain a complete and
accurate view of devices, users and applications with access to IT
systems
● Models that can detect and respond to deviations from the norm,
even with noisy data
● Prediction models that will assess where and how a company is
most likely to be breached, so planning and resource allocation
can be directed toward weak points in the IT system
● Explainability of model recommendations and analysis for
security operations leaders, CISOS, auditors and Board of
Directors
Business Value
● Get up to date knowledge of global and industry level threats to
help prioritize defense systems
● Prevent cyber threat incidents and respond quicker/better when
they do happen, improving OPEX
● Free up limited cybersecurity teams to focus on complicated
cases, while AI takes care of routine tasks
H2O.ai’s AI and Data Approaches
● Classification Models that can identify threatening vs non
threatening events and actors
● Anomaly detection, entry classification, domain generation
detection
● Unsupervised learning for unlabeled data, clustering data based
on anomalies
● Analyze large data sets of events to identify many different types
of threats (eg, malware, ransomware, email phishing, malicious
code downloads)
● Train neural networks to tell the difference between malicious
and safe files
● Use images to train classifier neural networks to detect malware
in .doc and .pdf files
● Bias reduction models
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Employee Retention
Objective
● Predict the risk of employee departure and identify relevant factors
● View predictions of employee departure, forecast churn rates and
identify relevant factors contained in employee data
Outcome
● Models with high accuracy of predicting employee departure and
identifying reasons for departure
Business Value
● Retain flight-risk talent and thereby improve per employee
efficiencies
● Minimize cost of employee replacement due to backfills
H2O’s AI and Data Approaches
● Automatic feature engineering, model validation, model tuning,
selection and deployment, machine learning interpretation (MLI),
recipes, time series and auto pipeline generation for model scoring
H2O AI App: Employee Churn Prediction
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H2O.ai Confidential
Precision Pricing
Objective
● Increase efficiency and accuracy of the entire pricing process
● React faster to rate changes with less actuarial and IT effort
● Improve model explainability and transparency for all lines of
business
Outcome
● Virtually eliminate model bias with automatic disparate impact
analysis for any model generated.
● Leverage new features gleaned from unstructured data through
OCR and NLP
● Reduce IT resources required to deploy and monitor models with
automated drift detection
● Automate the model documentation and filing process for state
regulators
Business Value
● Loss ratio improvement through enhanced segmentation and
targeted pricing
● Assure regulators and public that with unsurpassed model
explainability and interpretability
● Reduce risk and write more profitable, high quality business
H2O.ai AI and Data Approaches
● Automate the preparation of data pulled from from multiple
sources (eg, historical claims, policy and customer conversion and
retention data), with custom recipes to build models with the
insurance industry’s most widely used modeling platform
● Leverage automated feature engineering along with shapley
values for enhanced model interpretability and accuracy
● Provide product managers with “what if” pricing simulation AI
apps to predict impact of new pricing scenarios on profitability
and retention
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H2O.ai Confidential
Automated Underwriting
Objective
● Increase efficiency and accuracy of risk pricing process associated
with traditional manual underwriting processes, increasing
competitive advantage for insurers
● Widen the scope of data sources underwriters can use for
evaluation
Outcome
● Automate underwriting process including data collection, data
extraction, and filling forms
● Extract information from unstructured data through OCR and NLP
● Make models that can learn from 3rd party data sources, claims
histories and other past data to predict risk profile of new
submissions
● Deeper visibility into customer risk profiles
Business Value
● Tailor premiums to match each individual’s actual risk and optimize
pricing
● Shorten underwriting workflows creating near real-time service for
customer experience
● Improve underwriting profitability, while reducing OPEX, customer
churn, and costs for customer retention
H2O .ai AI and Data Approaches
● Use H2O recipes to automate the leveraging of data from
multiple sources (historical policy data, policyholder churn &
claims data to build models with LightGBM, Random Forest,
XGBoost and neural networks that outperform traditional GLM
approaches
● Train models with explainability and interpretability; understand
variable importance
● Use deep learning to process images and raw machine data
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H2O.ai Confidential
Reserve Prediction
Objective
● Use AI/Matching Learning to predict claim reserve amounts
based on historical based trends
● Accurately estimate the Reserve, and recalibrating these
estimates automatically when new information is added to the
claim record helps carriers react swiftly to ever changing events
Outcome
● Models that produce claims predictions that are aligned with actual
claims on an individual claim level, both in expected value and
variance.
Business Value
● Ensure that the size of reservoir of money Insurance Carriers hold is
adequately estimated the cost of future claims accurately, but not
excessive, to cover potential liabilities already assumed
● Improved transparency on future liabilities in order to ensure proper
statement of future liabilities on the balance sheet
H2O .ai AI and Data Approaches
● Machine learning techniques on a single, end-to-end platform
that leverages decision tree, SVM, neural network based and
deep learning based techniques
● Joint modeling of several numerical values (eg, paid losses and
claims outstanding)
● Make use of various claims data types (eg, numerical, categorical,
textual data) that can enhance prediction model understanding
and performance
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Customer Handling and Call Center Optimization
Objective
● Improve customer experience and reduce handling costs
● Map the query by the customer to appropriate resolution section
Outcome
● An improvised automated method of Exploratory Data Analysis that
segregates the data and provides numerical & graphical analysis
based on the factors such as Date/Time, Location, Self Made
Complaints, user profiles and past interactions.
● Improvised NLP techniques and real time processing can accurately
pinpoint/classify the complaints into groups.
● Send high risk, complex complaints to the more experienced
customer support agents.
● A multi-classification AI model to give suggestions for possible
resolution.
● The model can also be used as a backend mapping engine for
Chatbots and Customer Issue Resolution applications.
● The solution can be delivered through APIs and custom application
Business Value
● Address the complaints of customers, maintain trust and ultimately -
retain.
● Improve case deflection, the rate that customers are able to find
their own answers to issues that they would have otherwise called
support for.
H2O’s AI and Data Approaches
● Conduct Exploratory Data Analysis on Customer Complaint Data.
This data in the form of a Dataset will be Analysed, and numerical as
well as graphical analysis will be provided. Answers to questions
such as which location has the most complaints, what time period, is
the complaint made on behalf of the user or made by the customer,
etc. shall be provided in an organized manner.
● NLP Techniques will be used to process the data, which can be in
the form of a dataset or realtime.
● H2O AI App, built with H2O Wave, an SDK for Data Scientists:
○ Import Data and Data Prep ( Complaint Data)
○ Exploratory Data Analysis to analyse the aforementioned points.
○ Interactive Dashboard where insights are displayed based on the
searched tags, percentage rank of complaints ( #1 Rude
behaviour, #2 Issues, etc.) There will also be percentage
comparisons as the data increases ( Complaints in xxx domain
are 15% higher, etc.) Top increases, top decreases
○ Natural Language Processing Apps
● Applying NLP models to obtain tags based on meanings.
○ Text Sentiment Apps
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Claims Processing
Objective
● Infuse the claims processing workflow using AI to inform adjusters with
smart actions
● Quickly and accurately estimate and process claims
● Use AI to automate repetitive claims processes and streamline claims
processing, allowing those with low risk to be processed automatically
while higher risk claims are routed to investigators for review
● Predict claims payments at individual level
Outcome
● An improvised automated method of Exploratory Data Analysis that
segregates the data and provides numerical & graphical analysis based
on factors such as Date/Time, Location, Self Made Complaints, user
profiles and past interactions.
● An improvised automated method of Exploratory Data Analysis that
segregates the data and provides numerical & graphical analysis based
on factors such as Date/Time, Location, Self Made Complaints, user
profiles and past interactions.
● NLP techniques and real time processing can accurately
pinpoint/classify the complaints into groups.
● Send high risk, complex complaints to the more experienced customer
support agents.
● A multi-classification AI model to give suggestions for possible
resolution.
● The model can also be used as a backend mapping engine for Chatbots
and Customer Issue Resolution applications.
● The solution can be delivered through APIs and custom application
H2O’s AI and Data Approaches
● Build multiple models that can analyze all incoming structured and
unstructured data, scanned images, faxes and natural language sets,
policy information, medical bills reports, and legal documents to offer
real time recommendations to claims adjusters.
● Each time new claims data is added to a record, apply algorithms to
accurately score for fraud, reserve changes, and claim complexity at a
level of nuance that is often missed through manual reviews.
● Build models that can route high complexity claims to seasoned
adjuters for review, generating smart alerts with reason codes to take
immediate action; Low complexity, low fraud claims are automatically
flagged for straight through processing
Business Value
● Fewer touches
● Claims adjusters react fast to critical events
● Faster claims settlement
● Reduced ALAE
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Damage Detection and Estimation
Objective
● Increase granularity and accuracy for damage estimation
● Accurately detect and classify damages from images
Outcome
● Build models that can detect and classify damages from images,
isolating where in the image there is damage and what type of
damage is represented
● Translate damage into individual parts affected by adding additional
information to the analysis, such as parts listed needed to repair,
model optimum reconditioning scenarios
● Retrieve images of similar items (eg, vehicles), both damaged and
undamaged, to help estimators evaluate whether or not the item
was actually damaged
Business Value
● Shorten processing time for property and casualty damage claims,
improving customer satisfaction with faster, more accurate
settlements
● Equip claims estimators with signals to identify where to focus
attention during damage assessment, and reasons for it
● Route damage estimates to the right estimator team
H2O’s AI and Data Approaches
● H2O’s image recognition and deep learning capabilities that can
predict vehicle damage severity using multiclass classification
● Use binary classsification for vehicle damage identification
● Use multiclass classification of vehicle make, model, and year
● Use H2O Wave to make AI applications that allow business
stakeholders to get:
○ a pre-selection of cases where pictures do not match with
expectations
○ highlighted images where cases do not match claims reports
○ recommendations for next steps
● Explainable AI visualizations and narratives that explain why the
model made its damage estimation
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H2O.ai Confidential
Targeting
Objective
● Send messages/content at the right time with the right content
based on individual and collective customer behaviors
● Help marketing teams at FIs achieve more awareness, higher
conversions and better engagements
Outcome
● Pool large data sets to accurately predict custom audience
segments with specific attributes relevant to the brand
● Identify unique and relevant audiences for the brand that will be
in line with the audience data pools initially created
Business Value
● Increase click through rates by 4x (industry expectations)
● Reduce costs per engagement
H2O’s AI and Data Approaches
● Machine learning interpretability (MLI) that provides targeting
explanations
● Ability to provide computational power to advance granular
audience targeting capabilities
● Target identification models - automate and optimize processes
for potential consumer identification, information extraction and
market segmentation
● Probability models that will predict level at which consumers will
click on ads, click through rates, etc
● Recommendation models - discover useful patterns to determine
what the user finds interesting or not
● Machine learning interpretability that provides targeting
explanations
H2O AI Apps, built with H2O Wave, an SDK for Data Scientists:
● UMAP-based clustering App
● Customer Journey / Lead Scoring App
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Objective
● Expand offers to customers with accuracy and speed by
implementing predictive analytics
● Forecast behaviors of a target audience by analyzing their past
behaviors
Outcome
● Cut time to build models from 6 months to less than a week
● Doubled customer propensity to buy rate
● Realized additional revenue by being better able to target offers
● Created propensity models that helped companies identify the
right customers and prospects that have a high likelihood to
purchase a particular product or service
Business Value
● Optimize promotions based on knowing which leads to sell to
with the right message and product/services
H2O’s AI and Data Approaches
● Create models and use custom recipes that generate
features/variables that provide a probabilistic estimation of
whether customers will perform any of such actions or not i.e. a
propensity score.
● Use propensity scores to estimate value each customer brings in
real-time. Make data available to Relationship Managers of the
Bank (RMOs) who can further slice and dice the data and
consume the information intuitively.
● H2O AI Apps, built with SDK for Data Scientists, can perform:
○ Data Preparation
○ Supervised Machine Learning
● Automatic feature engineering, model tuning and
optimization, scoring pipeline generation
● Accurate time series capabilities
● Automatically generated visualizations and data plots
○ Nonlinear algorithmic modeling
○ Binary classification
○ Results viewed on Dashboards in a tabular format, grouped by
different features (city, areacode, spending limit, etc) or for a
single user; view target groups and selected features such as
textual insights explaining the prediction, the propensity
score for each user (mean or median) if a target group is
selected.
Propensity to Buy
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Customer Churn
Objective
● Predict customer churn based on behavioral patterns and
cadence of transactions
● Identify leading indicators for churn, improving customer
retention insights
Outcome
● Optimized models up to the granularity of each customer
● Calculated daily probability for customer churn for early
detection
● Distributed in-memory infrastructure trains and scores entire
customer base in minutes instead of hours
● Reduced model building time from 6-7 hours to < 30 min
Business Value
● Intercepted customer churn before it happened
● Know what leads a client towards the decision to leave the
company.
● Develop loyalty programs and retention campaigns to keep as
many customers as possible.
● Improving the customer retention rate for existing customers
by just 5 percent can improve a company’s profitability by 25 to
95 percent (Bain & Co).
H2O’s AI and Data Approaches
● Identify customers with high probability of churning by creating models and
custom recipes specifically built for generating features/variables.
● Distributed algorithms (Random Forest, GBM)
● Capabilities to iterate through models with different parameters within time
constraints, instead of relying on just one model
● H2O Customer Churn AI App, built with H2O Wave:
20. v
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Claims Fraud
Objective
● Find new ways to improve fraud detection accuracy and
detection time across claims fraud types
● Use AI to generate new fraud rules
● Detect anomalous patterns for individuals, accounts and
networks within and across portfolios
● Target different groups and subsets in a tailored approach,
comparing different inferences from these subsets with the
same inferences from the entire population
● Flag specific repetitive fraud groups and deploy counter
measures
Outcome
● 6x faster development of state-of-the-art ML models that
pre-empted fraudulent claims
H2O’s AI and Data Approaches
● Build AI models and use custom recipes specifically built for generating
features/variables that provide associated information about fraudulent
behavior. This data is then available to the fraud investigator who can further
slice and dice the data and consume the information intuitively.
● Feature engineering with Deep Learning to model new and complex attack
patterns quickly
● Behavior profiling for data networks - IP addresses, buying patterns
● Terabytes of data leveraged to deliver high scalability and performance, flexible
deployment and integration with other big data frameworks
● Model Explainability aids investigators in understanding the why the model
thinks there is potential fraud.
Business Value
● Reduce fraud losses and improve customer
experience
● 2x increase in accurate claims fraud detection
● 11% improvement in accuracy resulting in $1M
saved monthly per basis point
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Know your customer (KYC) Automation
Objective
● Operationalize customer data to understand behavior analytics for several use
cases across pricing, claims management, fraud prevention and more
● Identity verification
Outcome
● Create a foundation for customer analytics
● sA series of models that can assess customer analytics, behaviors and needs
across very different customer profiles
Business Value
● Improve campaign effectiveness
● Accurate, targeted cross-sell / up-sell
● Retain your most profitable customers
● Deliver real-time superior customer experience at all
points of service
● Increase customer lifetime value
● Verify that your customers are who they say they are
and assessing the risks associated with each customer
like the possibilities of fraud, money laundering,
terrorism financing, etc.
● Improve digital onboarding experience with automated
KYC processes, reducing application abandonment.
H2O’s AI and Data Approaches
● Process large quantities of customer data and integrate
into the H2O AI Cloud to build KYC models
● Probabilistic and Fuzzy Matching
● Document verification, NLP and image recognition to
verify and authenticate customer information
● Seamlessly extract customer data and integrate with
existing customer management or onboarding systems
Customer Data Integration
Social Profile Credit Profile Life Insurance
Profile
Mortgage Account
Profile
Bank Account
Profile
Customer
360º
View
Master Data Management
H2O AI Engine /
Probabilistic Matching
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Customer Behavior Analysis
Objective
● Segment users on the basis of different attributes such as -
demographics data, transaction history, transaction behavior
(Recency, Frequency, Monetary).
● Rapidly identify and evaluate customer behavior signals to take
action on potential fraudulent activity, optimize marketing
activities, etc
Outcomes
● Create behavior scoring models
● Continuously update customer data
Business Value
● Create foundation for:
○ Personalization strategies to improve customer experience,
customer sentiment
○ Targeting different groups in a tailored approach
○ Recommending core banking products for cross-selling and
up-selling, increase sales, revenues, and profits
○ Deploying customer churn prevention schemes
H2O’s AI, Data-Driven Approach:
● Provide an end-to-end pipeline that collects and updates the
customer data, performs data preparation, performs
unsupervised machine learning, and generates clustering results.
● Use H2O Wave (SDK for Data Scientists) to build an AI App that
can perform:
○ Data Preparation
○ Unsupervised Machine Learning
○ Rule fit analysis to identify cluster rules
○ Additional analysis such as association rules mining to identify
prominent rule patterns
○ Insights via a Clustering Dashboard
○ Tailored schemes of users
○ Graph Neural Network
○ Business metrics as defined by the end user need
○ Trigger retraining options