The document discusses DXC's approach to industrializing artificial intelligence (AI) services by taking AI projects from initial data stories through to fully operationalized solutions. It outlines DXC's methodology for mapping the AI journey, including developing data stories, running agile transformations, and building utility AI services. The document also discusses how DXC builds and manages industrialized data pipelines to integrate, enrich, and analyze data to create actionable insights through AI.
[AIIM17] Changing Times, The Future of ECM - Stephen LudlowAIIM International
It’s time to bring some clarity to the buzz and chatter surrounding ECM. What does the future of ECM look like? What’s behind the shift from “content management” to “content services?” And what should organizations be doing to take advantage of tomorrow’s opportunities? Join product leaders from OpenText and Documentum as they review the current state of ECM and lay out a go-forward strategy that maximizes current investments while preparing for future success.
Learn about the current state of Information Management in AIIM’s latest report: http://info.aiim.org/2017-state-of-information-management
Patterns provide structure and clarity, enabling architects to establish their solutions across the enterprise. Moreover, these software patterns also help to link technology and business requirements in an effective and efficient manner. Patterns help to incorporate robust solutions for business problems due to it’s wide adoption as well as it’s reusability. In addition, patterns create a common method to communicate, document and describe solutions. This session will explain some of these patterns ranging from SOA (Service-Oriented Architecture), WOA (Web-Oriented Architecture), EDA (Event Driven Architecture), and IoT (Internet of Things)
The Beauty of (Big) Data Privacy EngineeringDatabricks
Privacy engineering is an emerging discipline within the software and data engineering domains aiming to provide methodologies, tools, and techniques such that the engineered systems provide acceptable levels of privacy. In this talk, I will present our recent work on anonymization and privacy preserving analytics on large scale geo location datasets. In particular, the focus is on how to scale anonymization and geospatial analytics workloads with Spark, maximizing the performance by combining multi-dimensional spatial indexing with Spark in-memory computations.
[AIIM17] Changing Times, The Future of ECM - Stephen LudlowAIIM International
It’s time to bring some clarity to the buzz and chatter surrounding ECM. What does the future of ECM look like? What’s behind the shift from “content management” to “content services?” And what should organizations be doing to take advantage of tomorrow’s opportunities? Join product leaders from OpenText and Documentum as they review the current state of ECM and lay out a go-forward strategy that maximizes current investments while preparing for future success.
Learn about the current state of Information Management in AIIM’s latest report: http://info.aiim.org/2017-state-of-information-management
Patterns provide structure and clarity, enabling architects to establish their solutions across the enterprise. Moreover, these software patterns also help to link technology and business requirements in an effective and efficient manner. Patterns help to incorporate robust solutions for business problems due to it’s wide adoption as well as it’s reusability. In addition, patterns create a common method to communicate, document and describe solutions. This session will explain some of these patterns ranging from SOA (Service-Oriented Architecture), WOA (Web-Oriented Architecture), EDA (Event Driven Architecture), and IoT (Internet of Things)
The Beauty of (Big) Data Privacy EngineeringDatabricks
Privacy engineering is an emerging discipline within the software and data engineering domains aiming to provide methodologies, tools, and techniques such that the engineered systems provide acceptable levels of privacy. In this talk, I will present our recent work on anonymization and privacy preserving analytics on large scale geo location datasets. In particular, the focus is on how to scale anonymization and geospatial analytics workloads with Spark, maximizing the performance by combining multi-dimensional spatial indexing with Spark in-memory computations.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report...SlideTeam
Here we present One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report Infographic PPT PDF Document one pager template. You may be familiar with the word blockchain technology if you have been into investment, and banking. It is a record keeping technology that provides you a data structure assisting you in holding the transactional records ensuring transparency, security and decentralization. This bitcoin blockchain one pager PowerPoint template can be used to depict the entire concept of blockchain technology including its major components. This blockchain development one pager PowerPoint template covers the information about the project overview, and the objectives. You can also display how one can keep record of all the transactions within a business organization using digital ledger. This one pager has a section depicting the plan of action, and the key milestones. This blockchain technology PowerPoint slide also lets you present the project team, their names, designation and their roles and responsibilities. Jot down your past projects that you have been handling using this readily available customizable PowerPoint slide show. Add or delete the elements and make alterations in the slide in just few clicks. This slide can be completely edited from A to Z , its colours, to its texts and the movements of the objects everything can be altered. Grab this One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report Infographic PPT PDF Document one pager template now. https://bit.ly/3gZOm6S
M365 Saturday Saskatchewan 2020 - Build your #PowerPlatform #GovernanceNicolas Georgeault
Sites from my session:
Managing the Microsoft Power Platform platform can sometimes be very complex, and because your users have access to the various Power Apps and Power Automate options from other services, it's important to understand and understand the intricacies of the options. Understanding the differences in fault environments and others will allow you to better understand its management and better control the costs.
We'll also discuss the risks of letting unchecked developments proliferate at the risk of seeing a repeat of situations already encountered with Microsoft Access and Excel and some applications that have become critical in your business but completely absent from your service contract.
Cloud Modernization and Data as a Service OptionDenodo
Watch: https://bit.ly/2E99UNO
The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms. Cloud has become a key component of modern architecture design. Data lakes, IoT, NoSQL, SaaS, etc. coexist with relational databases to fuel the needs of modern analytics, ML and AI. Exploring and understanding the data available within your organization is a time-consuming task. And all of this without even knowing if that data will be useful at all.
Attend this session to learn:
- How dynamic data challenges and the speed of change requires a new approach to data architecture.
- Learn how logical data architecture can enable organizations to transition data faster to the cloud with zero downtime.
- Explore how data as a service and other API management capabilities is a must in a hybrid cloud environment.
A Reference Architecture for Digitalization in the Pharmaceutical IndustryCapgemini
A Reference Architecture for Digitalization in the Pharmaceutical Industry - Alina Chircu, Bentley University; Levent Sözer, Capgemini Germany; Eldar Sultanow, Capgemini Germany
INFORMATIK 2017
47. Jahrestagung der Gesellschaft für Informatik e.V. (GI) | 25.-29.9.2017 | Chemnitz
Workshop Enterprise Architecture Management in Forschung und Praxis
The hybrid cloud computing market is analyzed based on four segments: solutions, service model, verticals and regions. The solutions segment includes application architecture, network integration and management systems. Application architecture segment is expected to have a major role in the hybrid cloud computing market.
According to Infoholic research, the “Worldwide Hybrid Cloud Computing Market” is expected to grow at a CAGR of 34.3% during the forecast period 2016–2022.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
Role of Data in Digital TransformationVMware Tanzu
Data plays a big role in building the kinds of experiences demanded by the market today. In this session, we’ll unpack what goes into building a data-driven app, case studies of how organizations have successfully overcome siloed data and analytics to bring new predictive features into their applications, and what your next steps for data should be on your digital transformation journey.
Speaker: Les Klein, EMEA CTO Data, Pivotal
Denodo DataFest 2016: Metadata and Data: Search and ExplorationDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptQMW7
What matters the most for analysts and decision makers is finding the right data within seconds. Data virtualization incorporates a rich metadata catalog and graphical interface for the self-service users
In this session, you will learn:
• How to discover, search, explore, curate and share trusted data assets in a governed manner
• How to view and utilize the complete lineage of data assets
• Ways to infer patterns in data and metadata
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Accelerate Digital Transformation with an Enterprise Big Data FabricCambridge Semantics
In this webinar by Cambridge Semantics' VP of Solution Engineering, Ben Szekely, you will learn more about how the Enterprise Data Fabric prevails as the bedrock of enterprise digital strategy. Connected and highly available data is the new normal - powering analytics and AI. The data lake itself is commoditized, like raw compute or disk, and becomes an unseen part of the stack. Semantic graph technology is central to Data Fabric initiatives that meaningfully contribute to digital transformation.
We share our vision for digital innovation - a shift to something powerful, expedient and future-proof. The Data Fabric connects enterprise data for unprecedented access in an overlay fashion that does not disrupt current investments. Interconnected and reliable data drives business outcomes by automating scalable AI and ML efforts. Graph technology is the way forward to realize this future.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report...SlideTeam
Here we present One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report Infographic PPT PDF Document one pager template. You may be familiar with the word blockchain technology if you have been into investment, and banking. It is a record keeping technology that provides you a data structure assisting you in holding the transactional records ensuring transparency, security and decentralization. This bitcoin blockchain one pager PowerPoint template can be used to depict the entire concept of blockchain technology including its major components. This blockchain development one pager PowerPoint template covers the information about the project overview, and the objectives. You can also display how one can keep record of all the transactions within a business organization using digital ledger. This one pager has a section depicting the plan of action, and the key milestones. This blockchain technology PowerPoint slide also lets you present the project team, their names, designation and their roles and responsibilities. Jot down your past projects that you have been handling using this readily available customizable PowerPoint slide show. Add or delete the elements and make alterations in the slide in just few clicks. This slide can be completely edited from A to Z , its colours, to its texts and the movements of the objects everything can be altered. Grab this One Page Blockchain Theme Proposal For Distributed Ledger Presentation Report Infographic PPT PDF Document one pager template now. https://bit.ly/3gZOm6S
M365 Saturday Saskatchewan 2020 - Build your #PowerPlatform #GovernanceNicolas Georgeault
Sites from my session:
Managing the Microsoft Power Platform platform can sometimes be very complex, and because your users have access to the various Power Apps and Power Automate options from other services, it's important to understand and understand the intricacies of the options. Understanding the differences in fault environments and others will allow you to better understand its management and better control the costs.
We'll also discuss the risks of letting unchecked developments proliferate at the risk of seeing a repeat of situations already encountered with Microsoft Access and Excel and some applications that have become critical in your business but completely absent from your service contract.
Cloud Modernization and Data as a Service OptionDenodo
Watch: https://bit.ly/2E99UNO
The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms. Cloud has become a key component of modern architecture design. Data lakes, IoT, NoSQL, SaaS, etc. coexist with relational databases to fuel the needs of modern analytics, ML and AI. Exploring and understanding the data available within your organization is a time-consuming task. And all of this without even knowing if that data will be useful at all.
Attend this session to learn:
- How dynamic data challenges and the speed of change requires a new approach to data architecture.
- Learn how logical data architecture can enable organizations to transition data faster to the cloud with zero downtime.
- Explore how data as a service and other API management capabilities is a must in a hybrid cloud environment.
A Reference Architecture for Digitalization in the Pharmaceutical IndustryCapgemini
A Reference Architecture for Digitalization in the Pharmaceutical Industry - Alina Chircu, Bentley University; Levent Sözer, Capgemini Germany; Eldar Sultanow, Capgemini Germany
INFORMATIK 2017
47. Jahrestagung der Gesellschaft für Informatik e.V. (GI) | 25.-29.9.2017 | Chemnitz
Workshop Enterprise Architecture Management in Forschung und Praxis
The hybrid cloud computing market is analyzed based on four segments: solutions, service model, verticals and regions. The solutions segment includes application architecture, network integration and management systems. Application architecture segment is expected to have a major role in the hybrid cloud computing market.
According to Infoholic research, the “Worldwide Hybrid Cloud Computing Market” is expected to grow at a CAGR of 34.3% during the forecast period 2016–2022.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
Role of Data in Digital TransformationVMware Tanzu
Data plays a big role in building the kinds of experiences demanded by the market today. In this session, we’ll unpack what goes into building a data-driven app, case studies of how organizations have successfully overcome siloed data and analytics to bring new predictive features into their applications, and what your next steps for data should be on your digital transformation journey.
Speaker: Les Klein, EMEA CTO Data, Pivotal
Denodo DataFest 2016: Metadata and Data: Search and ExplorationDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptQMW7
What matters the most for analysts and decision makers is finding the right data within seconds. Data virtualization incorporates a rich metadata catalog and graphical interface for the self-service users
In this session, you will learn:
• How to discover, search, explore, curate and share trusted data assets in a governed manner
• How to view and utilize the complete lineage of data assets
• Ways to infer patterns in data and metadata
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Accelerate Digital Transformation with an Enterprise Big Data FabricCambridge Semantics
In this webinar by Cambridge Semantics' VP of Solution Engineering, Ben Szekely, you will learn more about how the Enterprise Data Fabric prevails as the bedrock of enterprise digital strategy. Connected and highly available data is the new normal - powering analytics and AI. The data lake itself is commoditized, like raw compute or disk, and becomes an unseen part of the stack. Semantic graph technology is central to Data Fabric initiatives that meaningfully contribute to digital transformation.
We share our vision for digital innovation - a shift to something powerful, expedient and future-proof. The Data Fabric connects enterprise data for unprecedented access in an overlay fashion that does not disrupt current investments. Interconnected and reliable data drives business outcomes by automating scalable AI and ML efforts. Graph technology is the way forward to realize this future.
The world of Microsoft Dynamics 365: a new day in your life with DynamicsDXC Eclipse
Microsoft Dynamics 365: Continue Your Transformation Journey.
The world of Microsoft Dynamics 365: a new day in your life with Dynamics
Today, Dynamics 365 delivers more than ever before. It offers the opportunity to take your workforce beyond the standard features of ERP and CRM with a new way of working that will transform your operations.
Presented by Abhinav Saxena - Practice Executive, Microsoft Dynamics 365, DXC Eclipse.
Opportunities and Pitfalls of Prototyping with Artificial Intelligence berl...DAIN Studios
How to build new products and services that respond intelligently to users and their contexts? When does it make sense to use AI in service design? DAIN Studios talks about Data Driven Design and the use of AI in design.
Big Data Brussels 2019 v.4.0 I 'How to Build Big Data Analytics Capabilities ...Dataconomy Media
One of the big challenges of organisations today, is leveraging analytics to convert Big data into actionable decisions. This necessarily goes through building the necessary capabilities. These capabilities need to be the right mix of People, Processes and Platforms. The talk will take each of these components and discuss them.
Top 10 tredning technologies to learn in 2021Lokesh Agarwal
In this world of digitalization, technologies are expanding rapidly. As the world foremost tech news contributor, it is the duty of us to keep everyone updated with the newest trends of the top 10 trending technologies in 2021. Technology and programming language are so important in day to day lifestyle to make the livelihood more facile. These computer scientists and professionals are regularly making the bests out of anything. Technology has taken a face of more productiveness and give the best to the nation. In the present scenario, everything is done through the technical process, you don’t have to bother about doing work, everything will be done automatically. In this article, some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top 10 trending technologies in2021 and its impression in the coming future.
Learn All about Data Science from the Best Private University in KarnatakaREVA University
Completing Masters in Data Science degree can reshape your career path, though it demands dedication and time to gain the necessary skills and land the right job. To assist you, we've crafted a detailed plan for building a career in Data Science.
As humans, we face an increasing amount of data and information every day. To derive meaning and make sense of this complex world, we constantly scan the world around us and select what we believe is important and what is not. In this session I will go trough the end to end framework about turning data into business actions.
IEEE 2019 Data Mini Projects for Btech & Mtech Students
Cloud Technologies providing Complete Solution for all
Academic Projects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Email ID: cloudtechnologiesprojects@gmail.com
2023 GEOINT Tutorial - Synthetic Data Tools for Computer Vision-Based AI - Re...Chris Andrews
The acquisition of labeled, unbiased, high quality remote sensing information for training AI systems is expensive, error prone, and sometimes impossible or dangerous. The efficacy of Remote Sensing and Imagery Analysis tools that use AI depends directly on the data used for training and validation, meaning that the cost and availability of data limits the application of AI for imagery exploitation. Synthetic Computer Vision (CV) data has become a strategy to reduce the cost and limitations of using real-world data in detection problems in data sparse domains. Focusing on remote sensing data including visible and invisible electromagnetic spectra, attendees will learn about the expanding options for generating synthetic data that are being used in commercial and academic domains, the technology options available for users who want to create CV content of a variety of types, and patterns of creating synthetic data to support
Learning Objectives
- Describe synthetic data including different types such as Generative AI and physics-based data
- Identify the opportunities for applying synthetic data in place of real sensor data
Will be able to describe the steps required to generate synthetic data for computer vision workflows from concept to production for training and validating AI.
- The intent of this class is to introduce the concepts and mechanisms behind the creation of synthetic data and to expose students to approaches for generating synthetic data using tools currently on the market.
- Familiarity with concepts around AI training and validation using remotely sensed data will be helpful for attendees.
The Agile Analyst: Solving the Data Problem with VirtualizationInside Analysis
The Briefing Room with Radiant Advisors and Cisco
Live Webcast Jan. 21, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=05e9d4ccbd2505ce15bc8de699f9c961
Today’s business analyst needs data from all kinds of places: the data warehouse, data marts, web services as well as local and departmental files and spreadsheets. The fact is, even seasoned analysts typically spend more than half their time hunting and gathering data, which impedes analytical insights and limits time to value. Increasingly, innovative organizations are turning to data virtualization as a faster path to analytics, thus expediting business impact.
Register for this episode of The Briefing Room to hear Analysts Lindy Ryan and John O'Brien of Radiant Advisors explain how analytical sandboxes and data virtualization can enable true analytic agility. They will be briefed by Marc Breissinger of Cisco Data Virtualization Business Unit, who will tout his company’s upcoming analytic platform Data Collage, a desktop tool for designed for analysts who need agile access to enterprise data. He will discuss how Data Collage allows users to easily combine data and accelerate the development of new analytics.
Visit InsideAnalysis.com for more information.
Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AIBigDataExpo
Dynniq is a high-tech, innovative company offering smart mobility solutions and services internationally. We will present advanced IoT use cases Dynniq is working on, and share how GoDataDriven helps set up an AI capability. We will share our learnings, and show what makes data science in the mobility domain unique.
A Data Driven Roadmap to Enterprise AI Strategy (Sponsored by Contino) - AWS ...Amazon Web Services
AI is transforming every aspect of our daily lives and the data landscape is becoming increasing open and transparent, thanks to the Consumer Data Right, most notably Open Banking. Between the high level academia and low level algorithms, where should the modern business leader start on their AI journey and harness true value from their data? Let us show you a step by step, data-driven approach towards enterprise-wide AI adoption.
In this world of digitalization, technologies are advancing rapidly. As the world’s foremost tech news contributor, it is our duty to keep everyone updated with the latest top 10 trending technologies in 2021.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
Arocom is a consulting and solution engineering company with expertise in providing engineering services for AI & Machine Learning, Data Operations & Analytics, MLOps and Cloud Computing.
Our clients include companies within biotech, drug discovery, therapeutics, manufacturing, retail and startups. Our consultants are best in their skills and offer hands-on talent to our clients in achieving their goals.
Similar to DXC Industrialized A.I. – Von der Data Story zum industrialisierten A.I. Service (20)
From leading IoT Protocols to Python Dashboarding_finalLukas Ott
First i like to give an overview on common IoT Protocols:
#CoAP (Constrained Application Protocol -> Close to HTTP / REST ) #MQTT ( Message Queue Telemetry Transport -> Pub/Sub with Broker -> Well defined Quality of Service -> Newest addition Eclipse Amlem (formerly the core of IBM Watson IoT platform) -> Eclipse Sparkplug -> Standardization of the topics and payloads -> Interoperability!) , #DDS (Data Distribution Service -> Pub/Sub without Broker -> Drones / Robotics) #LwM2M (Lightweight M2M -> Runs on Top of CoAP or MQTT -> standard sets of payloads for sensors) #zenoh (https://zenoh.io/ Pub/Sub Protocol -> combines the advantages of #DDS and #MQTT) #eclipsefoundation #apache #opensource #lightweight (+ some comments that this is not complete and does not encompass Industrial and Building Automation)
Then I would like to show the leading edge IoT protocol Zenoh. Saving Zenoh Payload to Apache IoTDB. After that I would like to dive into Panel and the awesome capabilities of Apache ECharts.
Platform Strategy and Data-driven development in Pharmaceutical IndustryLukas Ott
Pharmaceutical Research & Development 101
From Enterprise Architecture to platform strategy execution
Discussion on use cases development for data analytics
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
4. May 10, 2019 4DXC Proprietary and Confidential
PD_9991a-19
Cognitive Computing
Simulating, specifically, the perception and reasoning
aspects of human intelligence:
• Natural-Language Processing
• Speech
• Vision
Artificial Intelligence
Any program that does
something that we would think of
as intelligent in humans
AI is defined by the application of
the technology rather than the
technology itself. What is considered
AI may change over time.
AI at DXC:
• Extend Domain Expertise
• Perform Complex Planning
• Infer intent
Unsupervised Supervised
Discover new patterns Learn specific patterns
Cat
Not Cat
Artificial Intelligence
Machine Learning
Any program that improves
its performance through
experience rather than
explicit programming.
Deep Learning
Machine learning based on
neural networks
5. May 10, 2019 5DXC Proprietary and Confidential
PD_9991a-19
Strong versus weak A.I.
6. May 10, 2019 6DXC Proprietary and Confidential
PD_9991a-19
AI or ML?
Face detection Infer one is upset
Schedule a repair
(Predictive Maintenance)
Predict equipment breakdown
Sort documents by
topic (e.g. emails)
Cluster documents by similarity
AI
ML AI
ML
AI ML
8. May 10, 2019 8DXC Proprietary and Confidential
Enterprise-Scale
Data Science
Experience
Industrialized AI
Master
The Industrialized AI
Master journey
9. May 10, 2019 9DXC Proprietary and Confidential
Create Data Stories
Run Agile
Transformation
Industry Consulting Experience
Industrialized AI
Leader
The Industry Consultant
10. May 10, 2019 10DXC Proprietary and Confidential
Enterprise-Scale
Data Science
Experience
Industrialized AI
Master
Create Data
Stories
Run Agile
Transformation
Industry Consulting
Experience
Industrialized
AI Leader
The Industry Consultant
Journey
11. May 10, 2019 11DXC Proprietary and Confidential
A Common Mistake with AI Projects
• Without a hypothesis: convincing stakeholders to take the leap
Data Write
algorithms
Find
patterns
Tell a
story
Stakeholder
commitment
Big Gap
12. May 10, 2019 12DXC Proprietary and Confidential
From Ideas to Innovation
Buildathons are a great way to begin innovating with AI. We have created some effective formats to make
sure the best ideas are transformed into finalized products ready to be launched.
1
2 weeks
Preparation
2
48 hours
Buildathon
3
3 months
Industrialize
For 2 weeks leading up to a buildathon,
the preparation phase allows us to
collect data, ideate and refine ideas.
A post-buildathon phase where we
industrialize the solution through a
carefully structured AI innovation
program.
Teams composed of data scientists,
data engineers and analytics developers
create an AI solution in 48 hours around
a theme.
13. May 10, 2019 13DXC Proprietary and Confidential
Various skill sets of the DXC team
AI Leaders
Those who have sizzling ideas and
are looking for a team. They have
a vision of how AI can transform
the company.
Their talents include building,
testing and analyzing AI solutions.
Love collaboration and innovation.
Experts in munging data and
building analytics platforms. They
know how to scale AI and make an
impact.
They know how to build data-
driven apps that reach employees,
and change how business is done.
The secret ingredient to any team.
Data Scientists Data Engineers Analytics Developers
14. May 10, 2019 14DXC Proprietary and Confidential
PD_9991a-19
Analytical Layer Information Layer Operational Layer Benefit Layer
Ticket classification
Topic-based index system
Incident Ticket
Handbooks
NLP pipeline
Automatic ontology/
topic assignment
Optimized ServiceInstruction article +
recommendations
Solutioning support
Savings
Quality + Speed +
Efficiency Increase
Feedback loop
Customer
Satisfaction
50M IT tickets issued
At the forefront of technology, ITSM is
heavily impacted by the rapid change of
the IT landscape
10% less time spent
Assigning service tickets according to
their identified topic to specific teams,
obsoletes manual distribution.
10k pages of manuals read
Handbooks for knowledge manag
ment continue to be the
primary resource to go to
NLP
>95%classification accuracy
State of the art Deep Learning
classification algorithms achieve
highest scores in real-time
Digital Assistance for services like maintenance / other services
Enhance/ improve
ontology/ corpus
20% more efficiency
Ontologically indexed Knowledge Base
articles and article recommendations
boosts ticket-team productivity
15% cost savings
By cutting out inefficient workflow steps
through automation and efficiency gains
through digital assistance
25% more satisfaction
Faster ticket resolution and the
increased quality leads to increased
customer satisfaction
Version & Release Date index
System.
Feedback loop
15. May 10, 2019 15DXC Proprietary and Confidential
PD_9991a-19
What Clients See (The AI Market)
Platform
Product
“Solution”
Platform
Product
Platform
Product
16. May 10, 2019 16DXC Proprietary and Confidential
Enterprise-Scale
Data Science
Experience
Industrialized AI
Master
Run AI
experiment
Perform AI
Forensics
Machine Learning
Experience
Industrialized AI
Data Scientist
The data science
journey
17. May 10, 2019 17DXC Proprietary and Confidential
Approach using Python and RASA.io
Dataset
Questions, Answers, Links,
Tags, Importance, …
e.g. mechanics.
stackexchange.com
Dataset Data Preprocessing
From Posts.xml to a
cleaned and preprocessed dataframe
User
Feedback Loop
Machine Learning &
Application Development
Develop a Chatbot with Intent Recognition
with the aid of Natural Language
Processing & Understanding
19. May 10, 2019 19DXC Proprietary and Confidential
Seed Stage
• Curate the idea
Early Stage
• Solve the problem
Growth Stage
• Perfect the data supply chain
Maturity
• Automate the infrastructure
The AI Garage
• Virtual meetups
• Virtual Build-a-thons
Industrialized
Co-located
Build-a-thons
Focused Sprints Managed Services
Industrialized Industrialized
Iterations 0.X
functional
operational
managed
functional
operational
managed
functional
operational
managed
Iterations 1.1…1.X
Iterations 2…N
Give the Client an AI Startup Experience
Crawl Walk Run
Investment:
Participation
Investment:
~$100K - $400K
Investment:
~$0.5M-$1M
Investment:
~$5M
20. May 10, 2019 20DXC Proprietary and Confidential
Industrialized AI Strategy: Use AI to Open New Innovation Capacity
Genesis Custom built Product Commodity
Visibility
21. May 10, 2019 21DXC Proprietary and Confidential
Industrialized AI Strategy: Use AI to Open New Innovation Capacity
Genesis Custom built Product Commodity
Visibility
Mature, stable commodities
22. May 10, 2019 22DXC Proprietary and Confidential
Industrialized AI Strategy: Use AI to Open New Innovation Capacity
Genesis Custom built Product Commodity
Visibility
Highly
visible in
the
enterprise
23. May 10, 2019 23DXC Proprietary and Confidential
Enterprise-Scale
Data Science
Experience
Industrialized AI
Master
Build Utility AI
Services
Build data
pipelines
Industrialized AI
Data Engineer
Data Engineering
Experience
The data engineering
journey
24. May 10, 2019 24DXC Proprietary and Confidential
Data Science is OSEMN
You are awesome.
I am awesome.
Data Science is OSEMN.
OSEMN Pipeline
•O —Obtaining our data
•S — Scrubbing / Cleaning our data
•E —Exploring / Visualizing our data will allow us to
find patterns and trends
•M —Modeling our data will give us our predictive
power as a wizard
•N —Interpreting our data
25. May 10, 2019 25DXC Proprietary and Confidential
Obtain Your Data
Skills Required:
•Database Management
•Querying Relational Databases
•Retrieving Unstructured Data
•Distributed Storage
Extract the data into usable format
26. May 10, 2019 26DXC Proprietary and Confidential
Scrubbing / Cleaning Your Data
Objective:
•Examine the data: understand every feature you’re working with, identify errors,
missing values, and corrupt records
•Clean the data: throw away, replace, and/or fill missing values/errors
Skills Required:
•Scripting language
•Data Wrangling Tools
•Distributed Processing
27. May 10, 2019 27DXC Proprietary and Confidential
PD_9991a-19
Build and Manage Industrialized Data Pipelines
REST
API
Event
Queue
Establish
automated,
continuous and
secure access
to source data
REST
API
Streaming
data
REST
API
Realtime
Analytics
Batch data
REST
API
File/Data
store
REST
API
Batch
Analytics
Data AnalyticsData Engineering
Maintain a
comprehensive
set of data
pipelines
needed to
create and
validate
actionable
insight
Integrate data and
use automation to
maintain global
context
Match data to
expected format,
structure, schema,
and content
We work with
clients to…
Enrich data with
additional features
(and AI insights) that
increase the ability to
predict target
outcomes
REST
API
28. May 10, 2019 28DXC Proprietary and Confidential
PD_9991a-19
Real-Time Data
Historical
Ticket Dataset
Streaming
Ticket Data
AWS
CodeBuild
RASA
Runtime in
AWS ECS
Back-End
Inference
Parse text,
Determine intent
Real-Time
Data
Trained NLP Model
Performance MetricsBatch Data
Real-time ticket
intent prediction
Web Application
for analysis, visualization,
reporting of NLP data
NLP
predictions
AWS S3
Storage
Training Set
Test Set Train
Generate Model,
measure effectiveness
AWS
CloudWatch
Target
System
Current Model
AI Utility – Actual Deployment
Hypothesis:
We can use NLP technology to process service requests and determine the maintenance intent
Ingest Data Pipeline
Deploy and Secure
Analyze, Design, Train, Test, Score
29. May 10, 2019 29DXC Proprietary and Confidential
Exploring (Exploratory Data Analysis)
Objective:
•Find patterns in your data through visualizations and charts
•Extract features by using statistics to identify and test significant variables
Skills Required:
•Python
•R
•Inferential statistics
•Experimental Design
•Data Visualization
30. May 10, 2019 30DXC Proprietary and Confidential
Modeling (Machine Learning)
Objective:
•In-depth Analytics: create predictive models/algorithms
•Evaluate and refine the model
Skills Required:
•Machine Learning: Supervised/Unsupervised
algorithms
•Evaluation methods
•Machine Learning Libraries: Python (Sci-kit
Learn) / R (CARET)
•Linear algebra & Multivariate Calculus
31. May 10, 2019 31DXC Proprietary and Confidential
Best Practice: An Incremental, Agile Approach
Stakeholder
commitment
Hypothesis
Get data
Write algorithmsGenerate
evidence
Decide on
hypothesis credibility
✓
Take an action
Data science
Small 4-6 week sprints
Scale globally across
the enterprise
and adapt
to fluctuating
enterprise demand
Map to standard
concepts and
make insights
repeatableUse experiments to
produce reliable
measurable results
Produce insights
that can be
distributed and
used throughout
the enterprise
Data engineering
32. May 10, 2019 32DXC Proprietary and Confidential
Interpreting (Data Storytelling)
Objective:
•Identify business insights: return back to business problem
•Visualize your findings accordingly: keep it simple and priority driven
•Tell a clear and actionable story: effectively communicate to non-technical audience
Skills Required:
•Business Domain Knowledge
•Data Visualization Tools
•Communication: Presenting/Speaking &
Reporting/Writing
33. May 10, 2019 33DXC Proprietary and Confidential
AI - 4. Industrialize your AI
A. Operationalization (Managed Platform and Managed Security)
Use Case: productionize Sandbox-environment
B. Industrialization (AI Utility, Closed AI Loop)
Use Case: End-to-end Automation and Scalability
-> Example „Knowledge Management (KM) Article Prediction Mechanism”
a. Reduce human effort
b. Reduce incident resolution time
c. Enhance knowledge management
d. Enhance consistency of incident resolution
Results:
34. DXC Proprietary and Confidential May 10, 2019
AI Utility Process Flow & Model Management
Labeling Team
Created Arthur Shlain
romthe Noun Pro ect
NLP for
Intent Recognition
6) Determine
intent Test Results
1) Parse text with NLP
2) Create
Categorization Model
Tags.
Synonyms
3) Train Model
4) Validate
Categorization
Model
5) Operationalize
Model
7) Route to L2
support
Historical DB
with questions
and intent
Data Scientists
35. May 10, 2019 35DXC Proprietary and Confidential
PD_9991a-19
Real-Time Data
Historical
Ticket Dataset
Streaming
Ticket Data
AWS
CodeBuild
AWS ECS
(Docker)
Back-End
Inference
Parse text,
Determine intent
Real-Time
Data
Trained NLP Model
Performance MetricsBatch Data
Real-time ticket
intent prediction
Web Application
for analysis, visualization,
reporting of NLP data
NLP
predictions
AWS S3
Storage
Training Set
Test Set Train
Generate Model,
measure effectiveness
AWS
CloudWatch
Target
System
Current Model
A Simple NLP Pipeline using Rasa NLU
Hypothesis:
We can use NLP technology to process service requests and determine the maintenance intent
Only this part used for the current badge
pipeline:
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
intent_tokenization_flag: true
intent_split_symbol: "+"
36. May 10, 2019 36DXC Proprietary and Confidential
PD_9991a-19
• Make entry into the
cloud–with either
Amazon AWS,
Microsoft Azure, IBM
or HPE Helion Virtual
Private Cloud (VPC)–
uncomplicated and
unintimidating,
minimizing costly
learning steps through
a high-touch service
approach, not widely
available
Why choose DXC Technology?
• For production in a
hybrid model, DXC
advises and implements
deployments with a
range of options,
including HPE Helion
VPC, on-premises and
public cloud
environments.
• Our breadth of expertise
and methods provide
options few competitors
offer
• Get accelerators like
reference architectures
and deployment
automation, extended
analytic capability
options, blueprints and
runbooks that cover the
initial setup,
onboarding and
ongoing run with SLAs
• We provide the richest
standardized package
in the industry
• Best practices for analytic
applications and data
workload optimization –
DXC combines the long
term commitment to
business intelligence (BI)
and data management with
access to a broad variety
of real life cases.
• We have proven expertise
managing Hadoop, related
analytic technologies and
cloud native services for
enterprise solutions
Full service
enablement
Best Practices
and Expertise
Easy and safe
setup
Integration and
expansion
options
37. May 10, 2019 37DXC Proprietary and Confidential
Enterprise-Scale
Data Science
Experience
Industrialized AI
Master
Create Data
Stories
Run Agile
Transformation
Industry Consulting
Experience
Industrialized
AI Leader
Build Utility AI
Services
Build data
pipelines
Industrialized AI
Data Engineer
Data Engineering
Experience
Run AI
experiment
Perform AI
Forensics
Machine Learning
Experience
Industrialized AI
Data Scientist
DXC Industrialized AI
The journey has begun
Learning happens everywhere
38. May 10, 2019 38
Where are you on the journey to
industrialized AI?
Contact us for a free copy of the booklet