As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
Machine learning is permeating our world. As it gains wider adoption, what does it mean for assurance professionals? This session will help you cut through the buzzwords and discover how machine learning can be leveraged in audit and compliance.
After completing this session, you will be able to:
Understand the two groups of algorithms
Understand the machine learning process
Describe use cases in assurance and compliance
Know where to learn more about machine learning
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
Best practices in building machine learning models in Azure MLZeydy Ortiz, Ph. D.
Microsoft Azure ML Studio provides an easy-to-use interface to build and deploy machine learning models. However, the user must carefully select and configure the modules in order to derive meaningful results. In this presentation, I discuss a case study to highlight best practices in building machine learning models.
The deck describes:
The importance of Project Management in context of Data Science
The Spotle SMART (Specify-Measure-Analyse-Roll-out-Test) model for managing Data Science projects
The CRISP-DM standard for data mining and analytics projects
Stakeholders
Tools and Methodologies used in Data Science projects
Common risks in Data Science Projects
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
Machine learning is permeating our world. As it gains wider adoption, what does it mean for assurance professionals? This session will help you cut through the buzzwords and discover how machine learning can be leveraged in audit and compliance.
After completing this session, you will be able to:
Understand the two groups of algorithms
Understand the machine learning process
Describe use cases in assurance and compliance
Know where to learn more about machine learning
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
Best practices in building machine learning models in Azure MLZeydy Ortiz, Ph. D.
Microsoft Azure ML Studio provides an easy-to-use interface to build and deploy machine learning models. However, the user must carefully select and configure the modules in order to derive meaningful results. In this presentation, I discuss a case study to highlight best practices in building machine learning models.
The deck describes:
The importance of Project Management in context of Data Science
The Spotle SMART (Specify-Measure-Analyse-Roll-out-Test) model for managing Data Science projects
The CRISP-DM standard for data mining and analytics projects
Stakeholders
Tools and Methodologies used in Data Science projects
Common risks in Data Science Projects
The Machine Learning Audit. MIS ITAC 2017 KeynoteAndrew Clark
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
Presentation held during Belgrad AI's opening event in March 2019. Terminology and workflows for a regular ML or data science project, top points from selecting an AI project and a little framework to help you get everything on the same page for the top levels details of your ML project.
Ofer Ron, senior data scientist at LivePerson.
Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV
I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.
AI Foundations Course Module 1 - An AI Transformation JourneySri Ambati
The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs
Speakers:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Ingrid Burton (H2O.ai - CMO)
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
This presentation was made on June 16, 2020.
A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA
AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.
H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.
Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start.
About Parul Pandey:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/_RxH-bNRqp0
A Decade of Data Science. The Nationwide Journey
The Nationwide Enterprise Analytics Office (formerly Customer Insights and Analytics) has more than 10 years of experience in end-to-end data product development and system integration. The culture to attract, train and develop talent, the technical advancement to apply the new methods, the model factory to productionalize models, and responsive processes to measure business impact have all contributed to positive business outcomes as well as this team’s fast growth. In this talk, we will introduce Nationwide’s data science capabilities through case studies of a few data products they have built and deployed.
Bio: Weiyan Zhao is the Director of Data Science at Nationwide Insurance’s Enterprise Analytics Office. She currently leads a team of data scientists to provide enterprise solutions that drive business value and influence decisions through application of advanced statistical modeling and machine learning techniques. Previously, Weiyan served as an Analytics Manager at Chase, and as a Research Associate at Nationwide Children’s Hospital and at University of Texas at San Antonio. She received her PhD in Epidemiology and Statistics, and has been passionate about data and analytics throughout her career. Additionally, she is also a long term volunteer for different non-profit organizations to promote culture and diversity, and mentors young professionals.
Machine Learning for Auditors: What you need to know - ISACA North America CA...Andrew Clark
Machine learning is a hot topic in today's discourse with a myriad of economic and social implications. As it gains wider adoption, what does it mean for assurance professionals? With the proliferation of buzzwords and the black box nature of machine learning, Andrew will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality.
The session will include an overview and high-level implementation guidelines of the two main groups of machine learning algorithms, unsupervised, and supervised, and some example use cases in the auditing and compliance spheres for each. A breakdown of the various buzzwords, AI, cognitive computing, predictive modeling, will be provided, differentiating between fact, future and fiction. The presentation will conclude with a discussion of the need for assurance individuals to begin peaking inside the black box and become aware of the potential regulatory consequences of widespread machine learning.
By providing an overview of the machine learning landscape, the need for ‘black-box auditing’, use cases to take back to your businesses and recommendation on where to learn more about machine learning, this session will provide an overview of what you need to know to adapt and thrive in the machine age.
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
The Machine Learning Audit. MIS ITAC 2017 KeynoteAndrew Clark
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
Presentation held during Belgrad AI's opening event in March 2019. Terminology and workflows for a regular ML or data science project, top points from selecting an AI project and a little framework to help you get everything on the same page for the top levels details of your ML project.
Ofer Ron, senior data scientist at LivePerson.
Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV
I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.
AI Foundations Course Module 1 - An AI Transformation JourneySri Ambati
The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs
Speakers:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Ingrid Burton (H2O.ai - CMO)
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
This presentation was made on June 16, 2020.
A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA
AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.
H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.
Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start.
About Parul Pandey:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/_RxH-bNRqp0
A Decade of Data Science. The Nationwide Journey
The Nationwide Enterprise Analytics Office (formerly Customer Insights and Analytics) has more than 10 years of experience in end-to-end data product development and system integration. The culture to attract, train and develop talent, the technical advancement to apply the new methods, the model factory to productionalize models, and responsive processes to measure business impact have all contributed to positive business outcomes as well as this team’s fast growth. In this talk, we will introduce Nationwide’s data science capabilities through case studies of a few data products they have built and deployed.
Bio: Weiyan Zhao is the Director of Data Science at Nationwide Insurance’s Enterprise Analytics Office. She currently leads a team of data scientists to provide enterprise solutions that drive business value and influence decisions through application of advanced statistical modeling and machine learning techniques. Previously, Weiyan served as an Analytics Manager at Chase, and as a Research Associate at Nationwide Children’s Hospital and at University of Texas at San Antonio. She received her PhD in Epidemiology and Statistics, and has been passionate about data and analytics throughout her career. Additionally, she is also a long term volunteer for different non-profit organizations to promote culture and diversity, and mentors young professionals.
Machine Learning for Auditors: What you need to know - ISACA North America CA...Andrew Clark
Machine learning is a hot topic in today's discourse with a myriad of economic and social implications. As it gains wider adoption, what does it mean for assurance professionals? With the proliferation of buzzwords and the black box nature of machine learning, Andrew will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality.
The session will include an overview and high-level implementation guidelines of the two main groups of machine learning algorithms, unsupervised, and supervised, and some example use cases in the auditing and compliance spheres for each. A breakdown of the various buzzwords, AI, cognitive computing, predictive modeling, will be provided, differentiating between fact, future and fiction. The presentation will conclude with a discussion of the need for assurance individuals to begin peaking inside the black box and become aware of the potential regulatory consequences of widespread machine learning.
By providing an overview of the machine learning landscape, the need for ‘black-box auditing’, use cases to take back to your businesses and recommendation on where to learn more about machine learning, this session will provide an overview of what you need to know to adapt and thrive in the machine age.
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
Top Business Intelligence Trends for 2016 by Panorama SoftwarePanorama Software
10 top BI trends for 2016 – by Panorama
Its all about the insight
Visual perception rules
The learning suggestive system - AI gets real
The data product chain becomes democratized
Cloud (finally)
“Mobile”
Automated data integration
Interned of things data accelerating into reality
Hadoop accelerators are the last chance for Hadoop
Fading of the centralized on–premise DWH
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
How Machine Learning Will Transform FinanceRich Clayton
In this presentation you will learn about emerging technology and how it can be applied to finance function and how to start your journey of becoming an Adaptive Enterprise.
Similar to Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEARNING (20)
Blueprint Series: Banking In The Cloud – Ultra-high Reliability ArchitecturesMatt Stubbs
Data architecture for a challenger bank.Speaker: Jason Maude, Head of Technology Advocacy, Starling BankSpeaker Bio: Jason Maude is a coder, coach, and public speaker. He has over a decade of experience working in the financial sector, primarily in creating and delivering software. He is passionate about explaining complex technical concepts to those who are convinced that they won't be able to understand them. He currently works at Starling Bank as their Head of Technology Advocacy and host of the Starling podcast.Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.Meetup sponsored by DataStax.
Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive P...Matt Stubbs
Speaker: Cedrick Lunven, Developer Advocate, DataStax
Speaker Bio: Cedrick is a Developer Advocate at DataStax where he finds opportunities to share his passions by speaking about developing distributed architectures and implementing reference applications for developers. In 2013, he created FF4j, an open source framework for Feature Toggle which he still actively maintains. He is now contributor in JHipster team.
Talk Synopsis: We have all introduced more or less functional programming and asynchronous operations into our applications in order to speed up and distribute treatments (e.g., multi-threading, future, completableFuture, etc.). To build truly non-blocking components, optimize resource usage, and avoid "callback hell" you have to think reactive—everything is an event.
From the frontend UI to database communications, it’s now possible to develop Java applications as fully reactive with frameworks like Spring WebFlux and Reactor. With high throughput and tunable consistency, applications built on top of Apache Cassandra™ fit perfectly within this pattern.
DataStax has been developing Apache Cassandra drivers for years, and in the latest version of the enterprise driver we introduced reactive programming.
During this session we will migrate, step by step, a vanilla CRUD Java service (SpringBoot / SpringMVC) into reactive with both code review and live coding. Bring home a working project!
Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.
Meetup sponsored by DataStax.
Blueprint Series: Expedia Partner Solutions, Data PlatformMatt Stubbs
Join Anselmo for an engaging overview of the new end-to-end data architecture at Expedia Group, taking a journey through cloud and on-prem data lakes, real-time and batch processes and streamlined access for data producers and consumers. Find out how the new architecture unifies a complex mix of data sources and feeds the data science development cycle. Expedia might appear to be a market-leading travel company – in reality, it’s a highly successful technology and data science company.
Blueprint Series: Architecture Patterns for Implementing Serverless Microserv...Matt Stubbs
Richard Freeman talks about how the data science team at JustGiving built KOALA, a fully serverless stack for real-time web analytics capture, stream processing, metrics API, and storage service, supporting live data at scale from over 26M users. He discusses recent advances in serverless computing, and how you can implement traditionally container-based microservice patterns using serverless-based architectures instead. Deploying Serverless in your organisation can dramatically increase the delivery speed, productivity and flexibility of the development team, while reducing the overall running, DevOps and maintenance costs.
Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 12:30 - 13:00
Speaker: David Maitland
Organisation: Redis Labs
About: This session will cover the technology underpinning at the software infrastructure level required to deliver the instant experience to the end user and enterprises alike. Use cases and value derived by major brands will be shared in this insightful session based the world's most loved database REDIS.
Big Data LDN 2018: BIG DATA TOO SLOW? SPRINKLE IN SOME NOSQLMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Perry Krug
Organisation: Couchbase
About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.
Big Data LDN 2018: ENABLING DATA-DRIVEN DECISIONS WITH AUTOMATED INSIGHTSMatt Stubbs
Date: 13th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Charlotte Emms
Organisation: seenit
About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 12:30 - 13:00
Organisation: Immuta
About: Artificial intelligence is rising in importance, but it’s also increasingly at loggerheads with data protection regimes like the GDPR—or so it seems. In this talk, Sophie will explain where and how AI and GDPR conflict with one another, and how to resolve these tensions.
Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA ...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:50 - 12:20
Speaker: Mark Pritchard
Organisation: Denodo
About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure.
Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.
Big Data LDN 2018: TURNING MULTIPLE DATA LAKES INTO A UNIFIED ANALYTIC DATA L...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:10 - 11:40
Organisation: TIBCO
About: The big data phenomenon continues to accelerate, resulting in multiple data lakes at most organisations. However, according to Gartner, “Through 2019, 90% of the information assets from big data analytic efforts will be siloed and unusable across multiple business processes.”
Are you ready to unleash this data from these silos and deliver the insights your organisation needs to drive compelling customer experiences, innovative new products and optimized operations? In this session you will learn how to apply data virtualisation to: - Access, transform and deliver data from across your lakes, clouds and other data sources - Empower a range of analytic users and tools with all the data they need - Move rapidly to a modern and flexible data architecture for the long run In addition, you will see a demonstration of data virtualisation in action.
Big Data LDN 2018: CONSISTENT SECURITY, GOVERNANCE AND FLEXIBILITY FOR ALL WO...Matt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 12:30 - 13:00
Organisation: Cloudera
About: The growth of public cloud is reinforcing the need to think more carefully about taking a consistent approach to data governance as technology teams build out a flexible and agile infrastructure to meet the demands of the business.
Join this session to learn more about Cloudera's recommended approach for enterprise-grade security and governance and how to ensure a consistent framework across private, public and on-premises environments.
Big Data LDN 2018: MICROLISE: USING BIG DATA AND AI IN TRANSPORT AND LOGISTICSMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 11:10 - 11:40
Organisation: Microlise
About: Microlise are a leading provider of technology solutions to the transport and logistics industry worldwide. Discover how, with over 400,000 connected assets generating billions of messages a day, Microlise is evolving its platform to bring real-time analytics to its customers to improve safety, security and efficiency outcomes.
Big Data LDN 2018: EXPERIAN: MAXIMISE EVERY OPPORTUNITY IN THE BIG DATA UNIVERSEMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 10:30 - 11:00
Speaker: Anna Matty
Organisation: Experian
About: Today there is a widespread focus on the 'how' in relation to problem solving. How can we gain better knowledge of what consumers want, or need? How can we be more efficient, reduce the cost to serve, or grow the lifetime value of a customer? But, how do you move to a place where you are not only solving a problem, you are redesigning the entire strategic potential of that problem? You are being armed with insight on what the problem is.
Data and innovation offer huge potential to revolutionise all markets. There is an opportunity to be one step ahead of the need, to redesign journeys and enhance enterprise strategies. To do this you need access to the most advanced analytics but also the best quality, including variations and types of data, and then the technology that can act on this insight. Data science can present a unique opportunity for uncovered growth and accelerate your business through strategic innovation – fast. In this session you will hear more about how today's analytics can move from a single task, to an ongoing strategic opportunity. An opportunity that helps you move at the speed of the market and helps you maximise every opportunity.
Big Data LDN 2018: A LOOK INSIDE APPLIED MACHINE LEARNINGMatt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 13:10 - 13:40
Speaker: Brian Goral
Organisation: Cloudera
About: The field of machine learning (ML) ranges from the very practical and pragmatic to the highly theoretical and abstract. This talk describes several of the challenges facing organisations that want to leverage more of their data through ML, including some examples of the applied algorithms that are already delivering value in business contexts.
Big Data LDN 2018: DEUTSCHE BANK: THE PATH TO AUTOMATION IN A HIGHLY REGULATE...Matt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 12:30 - 13:00
Speaker: Paul Wilkinson, Naveen Gupta
Organisation: Cloudera
About: Investment banks are faced with some of the toughest regulatory requirements in the world. In a market where data is increasing and changing at extraordinary rates the journey with data governance never ends.
In this session, Deutsche Bank will share their journey with big data and explain some of the processes and techniques they have employed to prepare the bank for today’s challenges and tomorrow’s opportunities.
Brought to you by Naveen Gupta, VP Software Engineering, Deutsche Bank and Paul Wilkinson, Principal Solutions Architect, Cloudera.
Big Data LDN 2018: FROM PROLIFERATION TO PRODUCTIVITY: MACHINE LEARNING DATA ...Matt Stubbs
Date: 14th November 2018
Location: Self-Service Analytics Theatre
Time: 13:50 - 14:20
Speaker: Stephanie McReynolds
Organisation: Alation
About: Raw data is proliferating at an enormous rate. But so are our derived data assets - hundreds of dashboards, thousands of reports, millions of transformed data sets. Self-service analytics have ensured that this noise is making it increasingly hard to understand and trust data for decision-making. This trust gap is holding your organisation back from business outcomes.
European analytics leaders have found a way to close the gap between data and decision-making. From MunichRe to Pfizer and Daimler, analytics teams are adopting data catalogues for thousands of self-service analytics users.
Join us in this session to hear how data catalogues that activate data by incorporating machine learning can:
• Increase analyst productivity 20-40%
• Boost the understanding of the nuances of data and
• Establish trust in data-driven decisions with agile stewardship
Big Data LDN 2018: DATA APIS DON’T DISCRIMINATEMatt Stubbs
Date: 13th November 2018
Location: Self-Service Analytics Theatre
Time: 15:50 - 16:20
Speaker: Nishanth Kadiyala
Organisation: Progress
About: The exploding API economy, combined with an advanced analytics market projected to reach $30 billion by 2019, is forcing IT to expose more and more data through APIs. Business analysts, data engineers, and data scientists are still not happy because their needs never really made it into the existing API strategies. This is because most APIs are designed for application integration, but not for the data workers who are looking for APIs that facilitate direct data access to run complex analytics. Data APIs are specifically designed to provide that frictionless data access experience to support analytics across standard interoperable interfaces such as OData (REST) or ODBC/JDBC (SQL). Consider expanding your API strategy to service the developers with open analytics in this $30 billion market.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Influence of Marketing Strategy and Market Competition on Business Plan
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEARNING
1. Big Data LDN 2018
London | November 14th 2018
André Balleyguier
Chief Data Scientist EMEA at DataRobot
How automation can accelerate the
delivery of Machine Learning