"Open Source Machine Learning & Predictive APIs" - Alex Housley, Founder & CEO of Seldon @ PAPIs Connect, Valencia, 14th March 2016. http://www.papis.io/connect
Abstract: IT decision makers now face an unprecedented challenge — and opportunity — to help their organization build a one-to-one relationship with customers and gain actionable insights. Machine learning and deep learning technologies that were previously reserved for companies such as Google and Amazon are now open-source. But open source machine learning is a fast moving target, with game-changing developments even in the six months since PAPIs 2015. To follow on from my talk in Sydney about our journey taking Seldon from a closed predictive API to an open source machine learning platform, I will provide fresh insight with applied examples to help decision makers stay in control, and identify opportunities for value creation.
Seldon - Open Sourcing a Predictive API - Data Science London #ds_ldnAlex Housley
Slides from my talk at Data Science London on 25th August 2015 about our experience open sourcing Seldon, an enterprise-grade machine learning platform for developers and data scientists that was previously a "black box" API. This is a repeat of my #papis2015 talk with further information about how microservices enable you to extend Seldon with your own algorithms, and third-party libraries and APIs.
The algorithms that I ran through in the Q&A:
1. User Clusters - improve relevance in high churn services.
2. Tag Affinity - focused tag-based associations.
3. Latent Factor Models - best for lower churn service.
4. Item Activity Correlation - built for static slowly changing historical items.
5. Topic Models - built for sites needing long tail recommendation.
6. Association Rules - basket analysis used in e-commerce to suggest the next best action.
7. Content Similarity - built for services with rich metadata and high sparsity across items.
More information: http://www.seldon.io
Seldon Tech Docs: http://docs.seldon.io
Seldon on Github: https://github.com/SeldonIO/seldon-server
We're hiring: http://www.seldon.io/careers
London Futurists - The Future of AI & SustainabilityAlex Housley
Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment?
In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place.
The event was chaired by David Wood. The camera was operated by Kiran Manam.
For more details about this event, see https://www.meetup.com/London-Futuris....
For more information about Seldon, see https://www.seldon.io/.
To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.
6G WILL UNLOCK THE POWER OF AI-FOR-EVERYTHINGMahbubul Alam
We envision 6G to offer revolutionary transformation which will usher in an era of connected built-in intelligent applications, services, and networks that will auto-provision end-to-end systems to guaranteed quality of services for an agreed service level agreement, ultra-high-speed data rate, surpassing that of last-mile wired connectivity, perceived zero-latency & deterministic jitter for human safety and mission-critical applications, extremely high reliability for essential services, high spectrum-bands for haptic, holographic, extensive multimedia streaming and more, connected artificial intelligence for autonomous functions and future unknown use cases, etc.
6G will be a key enabler for equitable wealth distribution and a major driver for the green economy. It will unleash the full potential for Industrial Revolution IE 5.0 which will focus on the co-operation between human and machine, as human intelligence works in harmony with cognitive computing and machines performs mundane, repetitive, error-prone tasks. By putting humans back into industrial production with 6G enabled collaborative robots a.k.a Cobots, workers will be upskilled to provide value-added tasks in production such as setting the strategy, provide oversight and add creative input, leading to massive customization & personalization for customers. In this talk, we will examine the state of AI and its potential role in 6G.
Scott Montgomerie (Scope AR): AR’s Influence on the Workforce of Tomorrow: Jo...AugmentedWorldExpo
A talk from the Main Stage at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Scott Montgomerie (Scope AR): AR’s Influence on the Workforce of Tomorrow: Job Eliminator or Creator?
As the speed of technology continues to accelerate automation in the manufacturing world, the inevitable question of whether or not the “human touch” will become obsolete is top of mind for workers. The fear that robots and smart technologies will take everyone's jobs is prevalent, but not necessarily true. AR has the power to be a job creator, not a job eliminator. Its ability to make anyone an instant expert can in fact increase job security by quickly helping workers become more proficient in tasks. Learn about real-world use cases where AR is making people better, and safer, at their jobs, and explore why enterprises who create a workplace that’s augmented, not automated, will be the leaders of tomorrow.
https://awexr.com
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Mobile Labs: Supercharge Mobile App Testing - All About SpeedMobile Labs
Whether you’re manually testing your mobile apps or exploring test automation, you have the need for speed. But sometimes choppy graphics, lag time, and annoying network issues get in the way. What can a modern-day testing superhero do?
Review this presentation to discover how to supercharge your mobile app testing for higher quality and better mobile experiences. Save the day by implementing a super-powerful, super-fast, and completely reimagined mobile device cloud into your testing lab.
You will learn:
- How to harness the power of real-time manual testing
- Ways to dominate test automation with Appium for incredible performance
- Tips to speed up mobile app testing for superior mobile app experiences
Preparing your team for a new XR platform; 7 key take-awaysAugmentedWorldExpo
A talk from the Enterprise Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Preparing your team for a new XR platform; 7 key take-aways
Hans Wernke | Inhance Digital
Rodrick Lekey | Inhance Digital
The launch of a new XR platform often creates a great deal of excitement among institutional users and developers alike. After all, products such as Oculus, HoloLens and Magic Leap help us significantly improve the way we tell stories, train employees and address persistent maintenance challenges. However, for developers, each product launch presents its own unique challenges. The team has to familiarize itself with a new SDK, a new device and often a completely different way of structuring content. In this session, Rodrick Lekey and Hans Wernke, of Inhance Digital, an LA-based interactive marketing agency with 21+ years of experience, will share the first-hand perspective of such a team of developers. What did we learn? What challenges did we experience? What would we do differently the next time around?
https://awexr.com
Seldon - Open Sourcing a Predictive API - Data Science London #ds_ldnAlex Housley
Slides from my talk at Data Science London on 25th August 2015 about our experience open sourcing Seldon, an enterprise-grade machine learning platform for developers and data scientists that was previously a "black box" API. This is a repeat of my #papis2015 talk with further information about how microservices enable you to extend Seldon with your own algorithms, and third-party libraries and APIs.
The algorithms that I ran through in the Q&A:
1. User Clusters - improve relevance in high churn services.
2. Tag Affinity - focused tag-based associations.
3. Latent Factor Models - best for lower churn service.
4. Item Activity Correlation - built for static slowly changing historical items.
5. Topic Models - built for sites needing long tail recommendation.
6. Association Rules - basket analysis used in e-commerce to suggest the next best action.
7. Content Similarity - built for services with rich metadata and high sparsity across items.
More information: http://www.seldon.io
Seldon Tech Docs: http://docs.seldon.io
Seldon on Github: https://github.com/SeldonIO/seldon-server
We're hiring: http://www.seldon.io/careers
London Futurists - The Future of AI & SustainabilityAlex Housley
Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment?
In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place.
The event was chaired by David Wood. The camera was operated by Kiran Manam.
For more details about this event, see https://www.meetup.com/London-Futuris....
For more information about Seldon, see https://www.seldon.io/.
To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.
6G WILL UNLOCK THE POWER OF AI-FOR-EVERYTHINGMahbubul Alam
We envision 6G to offer revolutionary transformation which will usher in an era of connected built-in intelligent applications, services, and networks that will auto-provision end-to-end systems to guaranteed quality of services for an agreed service level agreement, ultra-high-speed data rate, surpassing that of last-mile wired connectivity, perceived zero-latency & deterministic jitter for human safety and mission-critical applications, extremely high reliability for essential services, high spectrum-bands for haptic, holographic, extensive multimedia streaming and more, connected artificial intelligence for autonomous functions and future unknown use cases, etc.
6G will be a key enabler for equitable wealth distribution and a major driver for the green economy. It will unleash the full potential for Industrial Revolution IE 5.0 which will focus on the co-operation between human and machine, as human intelligence works in harmony with cognitive computing and machines performs mundane, repetitive, error-prone tasks. By putting humans back into industrial production with 6G enabled collaborative robots a.k.a Cobots, workers will be upskilled to provide value-added tasks in production such as setting the strategy, provide oversight and add creative input, leading to massive customization & personalization for customers. In this talk, we will examine the state of AI and its potential role in 6G.
Scott Montgomerie (Scope AR): AR’s Influence on the Workforce of Tomorrow: Jo...AugmentedWorldExpo
A talk from the Main Stage at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Scott Montgomerie (Scope AR): AR’s Influence on the Workforce of Tomorrow: Job Eliminator or Creator?
As the speed of technology continues to accelerate automation in the manufacturing world, the inevitable question of whether or not the “human touch” will become obsolete is top of mind for workers. The fear that robots and smart technologies will take everyone's jobs is prevalent, but not necessarily true. AR has the power to be a job creator, not a job eliminator. Its ability to make anyone an instant expert can in fact increase job security by quickly helping workers become more proficient in tasks. Learn about real-world use cases where AR is making people better, and safer, at their jobs, and explore why enterprises who create a workplace that’s augmented, not automated, will be the leaders of tomorrow.
https://awexr.com
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Mobile Labs: Supercharge Mobile App Testing - All About SpeedMobile Labs
Whether you’re manually testing your mobile apps or exploring test automation, you have the need for speed. But sometimes choppy graphics, lag time, and annoying network issues get in the way. What can a modern-day testing superhero do?
Review this presentation to discover how to supercharge your mobile app testing for higher quality and better mobile experiences. Save the day by implementing a super-powerful, super-fast, and completely reimagined mobile device cloud into your testing lab.
You will learn:
- How to harness the power of real-time manual testing
- Ways to dominate test automation with Appium for incredible performance
- Tips to speed up mobile app testing for superior mobile app experiences
Preparing your team for a new XR platform; 7 key take-awaysAugmentedWorldExpo
A talk from the Enterprise Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Preparing your team for a new XR platform; 7 key take-aways
Hans Wernke | Inhance Digital
Rodrick Lekey | Inhance Digital
The launch of a new XR platform often creates a great deal of excitement among institutional users and developers alike. After all, products such as Oculus, HoloLens and Magic Leap help us significantly improve the way we tell stories, train employees and address persistent maintenance challenges. However, for developers, each product launch presents its own unique challenges. The team has to familiarize itself with a new SDK, a new device and often a completely different way of structuring content. In this session, Rodrick Lekey and Hans Wernke, of Inhance Digital, an LA-based interactive marketing agency with 21+ years of experience, will share the first-hand perspective of such a team of developers. What did we learn? What challenges did we experience? What would we do differently the next time around?
https://awexr.com
Collaborative Robots 101: The Anatomy of a CobotSICK Inc
A collaborative robot—also referred to as a “cobot”— is a robot designed for interaction with a human. Check out the infographic to learn more about what makes a cobot and how the the technology is moving forward!
Jian Liang (HiScene): AR for Industry in China: From Concepts to Real Applica...AugmentedWorldExpo
A talk from the XR Enablement Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Jian Liang (HiScene): AR for Industry in China: From Concepts to Real Applications
AI/AR industry has attracted attention never seen before of academia and industry, into which numerous talents and resources have been invested. However, academic achievements are not equal to products, which need to be adjusted and optimized in technology, engineering, product, etc. according to specific application scenarios. This talk will share with you some difficulties, misconceptions and experience in commercializing AR based on HiScene’s practice.
https://awexr.com
Sascha Goldner (Airbus Defence and Space): A Case-Study for a UAV Operator En...AugmentedWorldExpo
A talk from the Work Track at AWE USA 2018 - the World's #1 XR Conference & Expo in Santa Clara, California May 30- June 1, 2018.
Sascha Goldner (Airbus Defence and Space): A Case-Study for a UAV Operator Environment Based on Mixed Reality
How can a consumer technology like VR/AR “rolled out” in a traditional and in some aspects even conservative company like Airbus Defence and Space? Beside of answering this question, a case-study shall be presented in which Mixed Reality is used in order to provide an enhanced and very isolated environment for a UAV operator.
http://AugmentedWorldExpo.com
A talk from the Creator Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Google: Rapid Prototyping for AR
Austin McCasland | Google
Diane Wang | Google
With many unknowns and opportunities in AR, rapid prototyping has been critical to understand and define what makes immersive experiences valuable and usable. This talk will take a peek into how we approach rapidly designing and building prototypes for AR applications.
https://awexr.com
AI techniques in construction industry.Khaled gharib
Giving notes and information about the artificial intelligence and machine learning usage and impact on the construction industry and how does it help the engineers/architects in automating their repetitive tasks through the project life cycle
Engineering.com webinar: Real-time 3D and digital twins: The power of a virtu...Unity Technologies
From buildings and infrastructure to industrial machinery and factories, digital twins are becoming integral across the industrial sector. In this webinar, first shown on Engineering.com, leaders from Unity and Unit040, provider of digital twin platform Prespective, share how digital twins add value at all stages of the project and product lifecycle, from the early stages of design to predictive maintenance using IoT data.
Watch the webinar here: create.unity3d.com/real-time-3d-and-digital-twins
ThingWorx Manufacturing Apps Enable Improved Operational Performance and Accelerated Time-to-Value
Boston, Mass. – May 23, 2017 –– PTC (NASDAQ: PTC) today announced at LiveWorx®17 the launch of new ThingWorx® manufacturing apps. The new role-based manufacturing apps are fast to deploy and provide industrial companies with real-time operational intelligence to make more proactive and faster decisions.
1. Having 3.8 years of testing experience which includes Manual , automation,mobile, API testing.
2. Experience in Mobile Testing include Functional Testing & GUI testing.
3 Hands on experience on Agile methodology.
4. Hands on domain experience Telecom, Mobility, retail, real-estate.
5. Basic domain knowledge of healthcare, banking and finance.
6. Hands on experience on SQL, JIRA, HP QC.
Find out what testing works for your mobile app.
Agile Software Development means we want to maximise progress while minimising waste. Delays cause waste, for instance wasted time and efforts; ineffective work causes waste; poor quality causes waste; and bugs cause waste and delay progress, etc.
Mobile apps and the mobile app ecosystem help determine what sorts of testing will be more valuable for the project. This workshop introduces various key concepts and factors related to testing mobile apps effectively. You will have the opportunity to practice testing mobile apps during the workshop to help reinforce your learning and discovery.
We will cover both interactive and automated testing of mobile apps, and find ways to reduce the Time To Useful Feedback (TTUF) so the project team can make more progress while reducing project waste. We will also cover various ways to gather more and better information about the qualities of our mobile codebase and of the quality of the apps-in-use.
Bring your mobile apps and mobile devices and be prepared to get involved in testing!
More details: http://confengine.com/agile-pune-2014/proposal/861/agile-mobile-testing
Conference: http://pune.agileindia.org/
IBM's DevOps solution for CLM includes a full lifecycle suite of products for managing continuous business planning, Agile project management, continuous build, source code management, test management, and continuous application monitoring.
Ensuring Maximum Quality in the Era of IoT and WearablesJosiah Renaudin
Until recently, the Internet of Things (IoT) was just an idea that techies talked about. Unlike innovations in the past, development and testing of the IoT is significantly more elaborate. After introducing the technology of wearables and IoT, Gauri Arondekar delves into the components and architectures that make it work. Focusing on tools and solutions that accelerate the testing processes, Gauri shares the success story of an end-to-end testing strategy for a leading provider of digital fitness solutions. Gauri describes how her team helped Peloton Cycle ensure an uninterrupted and seamless user experience. The Peloton bike, as part of an IoT ecosystem, required a new testing strategy to achieve the required quality. Automated tests became fundamental to the project, which also required a rapid, comprehensive study of technologies along with out-of-the-box test methodologies to simulate a user’s fitness session. In the course of the project, the team reduced testing cycles from one-to-two weeks to two-to-three days and developed comprehensive test automation for a native app on a custom tablet. Join Gauri as she describes the journey to deliver an IoT test strategy and the challenges they tackled along the way.
As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects?
Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for:
1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis.
2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics.
3) Operationalization of AI projects: challenges and best practices.
SA 2014 - Integrating the heterogeneous enterpriseDavid Graham
Mulesoft Connect Content presented in South Africa in November 2014. Unpacking the trends that are influencing the connected renaissance across systems whether they are on premise; off-site or in the cloud.
Persona-based testing has never been easier
Covered in this webinar:
- Intro to Perfecto
- Digital Challenges
- Perfecto CQ Lab and Wind Tunnel™ Overview
- Wind Tunnel Deep Dive
- How to Add Wind Tunnel to your Test
- Demo
- Q&A
By the end of this webinar, you'll be a master at adding UX to each of your tests!
Collaborative Robots 101: The Anatomy of a CobotSICK Inc
A collaborative robot—also referred to as a “cobot”— is a robot designed for interaction with a human. Check out the infographic to learn more about what makes a cobot and how the the technology is moving forward!
Jian Liang (HiScene): AR for Industry in China: From Concepts to Real Applica...AugmentedWorldExpo
A talk from the XR Enablement Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Jian Liang (HiScene): AR for Industry in China: From Concepts to Real Applications
AI/AR industry has attracted attention never seen before of academia and industry, into which numerous talents and resources have been invested. However, academic achievements are not equal to products, which need to be adjusted and optimized in technology, engineering, product, etc. according to specific application scenarios. This talk will share with you some difficulties, misconceptions and experience in commercializing AR based on HiScene’s practice.
https://awexr.com
Sascha Goldner (Airbus Defence and Space): A Case-Study for a UAV Operator En...AugmentedWorldExpo
A talk from the Work Track at AWE USA 2018 - the World's #1 XR Conference & Expo in Santa Clara, California May 30- June 1, 2018.
Sascha Goldner (Airbus Defence and Space): A Case-Study for a UAV Operator Environment Based on Mixed Reality
How can a consumer technology like VR/AR “rolled out” in a traditional and in some aspects even conservative company like Airbus Defence and Space? Beside of answering this question, a case-study shall be presented in which Mixed Reality is used in order to provide an enhanced and very isolated environment for a UAV operator.
http://AugmentedWorldExpo.com
A talk from the Creator Track at AWE USA 2019 - the World's #1 XR Conference & Expo in Santa Clara, California May 29-31, 2019.
Google: Rapid Prototyping for AR
Austin McCasland | Google
Diane Wang | Google
With many unknowns and opportunities in AR, rapid prototyping has been critical to understand and define what makes immersive experiences valuable and usable. This talk will take a peek into how we approach rapidly designing and building prototypes for AR applications.
https://awexr.com
AI techniques in construction industry.Khaled gharib
Giving notes and information about the artificial intelligence and machine learning usage and impact on the construction industry and how does it help the engineers/architects in automating their repetitive tasks through the project life cycle
Engineering.com webinar: Real-time 3D and digital twins: The power of a virtu...Unity Technologies
From buildings and infrastructure to industrial machinery and factories, digital twins are becoming integral across the industrial sector. In this webinar, first shown on Engineering.com, leaders from Unity and Unit040, provider of digital twin platform Prespective, share how digital twins add value at all stages of the project and product lifecycle, from the early stages of design to predictive maintenance using IoT data.
Watch the webinar here: create.unity3d.com/real-time-3d-and-digital-twins
ThingWorx Manufacturing Apps Enable Improved Operational Performance and Accelerated Time-to-Value
Boston, Mass. – May 23, 2017 –– PTC (NASDAQ: PTC) today announced at LiveWorx®17 the launch of new ThingWorx® manufacturing apps. The new role-based manufacturing apps are fast to deploy and provide industrial companies with real-time operational intelligence to make more proactive and faster decisions.
1. Having 3.8 years of testing experience which includes Manual , automation,mobile, API testing.
2. Experience in Mobile Testing include Functional Testing & GUI testing.
3 Hands on experience on Agile methodology.
4. Hands on domain experience Telecom, Mobility, retail, real-estate.
5. Basic domain knowledge of healthcare, banking and finance.
6. Hands on experience on SQL, JIRA, HP QC.
Find out what testing works for your mobile app.
Agile Software Development means we want to maximise progress while minimising waste. Delays cause waste, for instance wasted time and efforts; ineffective work causes waste; poor quality causes waste; and bugs cause waste and delay progress, etc.
Mobile apps and the mobile app ecosystem help determine what sorts of testing will be more valuable for the project. This workshop introduces various key concepts and factors related to testing mobile apps effectively. You will have the opportunity to practice testing mobile apps during the workshop to help reinforce your learning and discovery.
We will cover both interactive and automated testing of mobile apps, and find ways to reduce the Time To Useful Feedback (TTUF) so the project team can make more progress while reducing project waste. We will also cover various ways to gather more and better information about the qualities of our mobile codebase and of the quality of the apps-in-use.
Bring your mobile apps and mobile devices and be prepared to get involved in testing!
More details: http://confengine.com/agile-pune-2014/proposal/861/agile-mobile-testing
Conference: http://pune.agileindia.org/
IBM's DevOps solution for CLM includes a full lifecycle suite of products for managing continuous business planning, Agile project management, continuous build, source code management, test management, and continuous application monitoring.
Ensuring Maximum Quality in the Era of IoT and WearablesJosiah Renaudin
Until recently, the Internet of Things (IoT) was just an idea that techies talked about. Unlike innovations in the past, development and testing of the IoT is significantly more elaborate. After introducing the technology of wearables and IoT, Gauri Arondekar delves into the components and architectures that make it work. Focusing on tools and solutions that accelerate the testing processes, Gauri shares the success story of an end-to-end testing strategy for a leading provider of digital fitness solutions. Gauri describes how her team helped Peloton Cycle ensure an uninterrupted and seamless user experience. The Peloton bike, as part of an IoT ecosystem, required a new testing strategy to achieve the required quality. Automated tests became fundamental to the project, which also required a rapid, comprehensive study of technologies along with out-of-the-box test methodologies to simulate a user’s fitness session. In the course of the project, the team reduced testing cycles from one-to-two weeks to two-to-three days and developed comprehensive test automation for a native app on a custom tablet. Join Gauri as she describes the journey to deliver an IoT test strategy and the challenges they tackled along the way.
As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects?
Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for:
1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis.
2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics.
3) Operationalization of AI projects: challenges and best practices.
SA 2014 - Integrating the heterogeneous enterpriseDavid Graham
Mulesoft Connect Content presented in South Africa in November 2014. Unpacking the trends that are influencing the connected renaissance across systems whether they are on premise; off-site or in the cloud.
Persona-based testing has never been easier
Covered in this webinar:
- Intro to Perfecto
- Digital Challenges
- Perfecto CQ Lab and Wind Tunnel™ Overview
- Wind Tunnel Deep Dive
- How to Add Wind Tunnel to your Test
- Demo
- Q&A
By the end of this webinar, you'll be a master at adding UX to each of your tests!
Integrating Azure Machine Learning and Predictive Analytics with SharePoint O...Bhakthi Liyanage
Windows Azure Machine Learning and Data Analytics platform offers a streamlined experience, from setting up with only a web browser to using drag-and-drop gestures and simple data-flow graphs to set up experiments. Azure Machine Learning Studio features a library of time-saving sample experiments, R and Python packages, and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Learn how the Azure Machine Learning service in the cloud lets you easily build, deploy, and share advanced analytics solutions into your SharePoint platform. Attendees will also gain knowledge on special considerations that should be taken in to account when creating analytical models. The demo will walk you through creating an analytic model in Azure ML studio and consume the model within SharePoint online.
Unifying feature management with experiments - Server Side Webinar (1).pdfVWO
What’s common across companies like Netflix, Airbnb, Amazon, and Google? - Their ability to continuously experiment and roll out enhancements quickly across all aspects of their business. Today, every business is fundamentally a technology business. To stay ahead of the competition, you must iterate quickly with experiments across configurations in search algorithms, navigation, checkout flows, discounts, and security settings without breaking existing experience. That’s precisely what unifying experimentation with feature management enables for you.
Join Thejas Sridhar, Manager of Product Marketing, to explore the power of unifying A/B testing and feature management for continuous development.
Why Apps Succeed: 4 Keys to Winning the Digital Quality GamePerfecto by Perforce
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
Scope:
Share the key takeaways after migrating or modernizing several Progress character UI/desktop legacy applications.
Key Elements:
- What could be the business cases for taking action in “upgrading” a Progress character UI/desktop application?
- What are main the strategies that can be followed?
- What are the Progress tools that can help out in taking the approach on fast forward?
- What could be the long-term vision taking into account the business drives and the technology trends?
Key Takeaways:
- In which direction should I go with my Progress character/desktop UI app?
- What are the Progress tools and processes that can help out in this journey?
2016 Federal User Group Conference - DevOps Product StrategyCollabNet
Presented by Eric Robertson, General Manager of the DevOps Product Line at CollabNet, at the Federal User Group Conference on April 28th, 2016 in Washington DC.
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
Why Apps Succeed: 4 Keys to Winning the Digital Quality GameAustin Marie Gay
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
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- Intro to Perfecto
- Digital Challenges
- Understanding the Perfecto CQ Lab
- Architecture/Setup
- Perfecto University, Community and Partner Portal
- Perfecto Solution Look and Feel
- Q&A
By the end of this webinar, you'll have a solid foundation of Perfecto's tools, resources, and products.
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5. SELDON.IO
Our Journey
2011 2014 2015 2016
Content
Recommendation
Social Sharing
Data scientists in
short supply
Organizations want
more control and on
premise solutions
Open Source Machine
Learning
Recommendation and Prediction
Microservices API
Platform agnostic with no lock-in
Deploy on premise or in the
cloud
FinTech Products
Barclays
Proof of Concert
Machine Intelligence
Ecosystem
+
6. SELDON.IO
Economic Social Technological
2016
the breakout year for open machine intelligence
● Lower compute costs
(CPU and GPU)
● Disruptive start-ups
● Data privacy and
compliance
● Consumer expectations
● Workforce automation:
58% of job activities.
● Data scientists and
decision makers want
control
● Commodification of
ML/AI technologies
● 2 billion smartphones;
13 billion connected
devices.
● Exponential data
9. SELDON.IO
How to add machine intelligence to your company
Build In-House
Predictive API
Open Source
Machine
Learning
Control Model
Evaluation
Time Data Scientists Cost
$$$✔️
Too Many
$$✗
Limited /
Unknown
$✔️
Industry Models +
Your Own
14. SELDON.IO
1. User Clusters - improve relevance in high churn services.
2. Tag Affinity - focused tag-based associations.
3. Latent Factor Models - best for lower churn service.
4. Item Activity Correlation - built for static slowly changing historical items.
5. Topic Models - built for sites needing long tail recommendation.
6. Association Rules - basket analysis to suggest the next best action.
7. Content Similarity - rich metadata and high sparsity across items.
Recommendation Algorithms
15. SELDON.IO
• Cascade/combine multiple algorithms to cover different users and use
cases
• control relevance, popularity, diversity
• control interactiveness of recommendations
• Combine algorithm results - e.g. weighted scores, rank combine.
• Run A/B and Multivariate tests with no redeploy
• Select algorithm strategies via API tags
• to handle user cohorts: mobile users, desktop, tablet
• to provide multiple content recommendations per page: site-wide, intersection
• Change all configuration in real time with no redeployment.
Advanced Optimization
16. SELDON.IO
start
test
best
1
2
3
N
Selecting the best model
● Evaluation of multiple
strategies in parallel using
multi-armed bandit.
● Adaptive as context
changes - i.e. time of day,
special event.
● The latest winning test
strategy (1...N) is promoted
to best.
17. SELDON.IO
• Stream events in real-time
• (i.e. metadata associated with transactions)
• Create supervised learning pipelines:
• Classification - yes/no (binary) or categorize (multi-class)
• Regression - predicting a continuous value
• Pluggable Algorithms
• Vowpal Wabbit
• XGBoost
• Keras
• Your algorithm!
General Purpose Prediction
22. SELDON.IO
Seldon 2.0
• Flexible design
• Apache Kafka as hub
• data pushed to DBs and
processing units as needed
• Stream algorithms via Flink (or
Spark) dependent on latency
requirements
• Batch algorithms via Spark (or
Flink)
• Low latency front end scoring
Zookeeper for state and control
• Luigi pipeline
• Docker Swarm for deployment
• Python single machine for agile
algorithm development
23. SELDON.IO
2016 is the breakout year for
open source machine intelligence.
Data scientists and decision makers
want more control.
Open source helps organizations focus
on the last 10% of the problem.
26. SELDON.IO
How do you make money?(the elephant in the room)
OS
Integration Services
SaaS
Marketplace
Editor's Notes
Install the Lato font for the best experience: http://www.fontsquirrel.com/fonts/LATO
My name is Alex Housley founder and CEO, Seldon.
The last time I gave this talk was in Sydney, so I’m happy to halve my carbon footprint by having the chance to share this with the PAPIs community in Valencia. Promise I’ll travel by bicycle when PAPIs comes to London.
We released Seldon’s open source predictive platform in Feb last year after four years of R&D from an exceptional team of data scientists.
Seldon is tried and tested as a closed API in demanding enterprise environment serving billions of recommendations every month.
I will talk about how OS helped us to survive and thrive.
This is not meant to be a an evangelical talk about how I have “seen the light”, I want you share with you some of the things we have learned from moving closed to open.
[set the scene] You’re in a hot, Sweaty board room, mid-Summer,
We were a SaaS predictive API with two PRODUCTS: Recommendation Engine and Predictive Social Sharing.
FACED WITH 2 OPTIONS:
1. GET ACQUI-HIRED
2. DO SOMETHING DIFFERENT
We had spent the prev 9 months speaking to potential acquirers. During this process I had a front row seat on the priorities of some of world’s leading media, ecommerce, technology companies.
COMMON PATTERN emerged: large companies putting data science and prediction at the very top if their strategic agenda for investment. They were acquisitive and recruiting expensive teams.
Made me think: WHAT WOULD BE THE IMPACT OF MAKING THE FULL PREDICTIVE STACK OPEN SOURCE?
On a wet and windy day in October 2014, our team were sitting on Brighton Beach, discussing the bigger picture of what we could achieve with Seldon.
We had come a long way over the previous three years and were serving content recommendations to hundreds of millions of people every month.
However, we believed that continuing to ship a black box solution would increasingly face obstacles in adoption by enterprises as machine learning technology became increasingly commoditized, and new applications were developed and adopted everywhere.
So we did the most disruptive thing we could imagine and open-sourced the platform and algorithms that we had spent many years, and a couple of million pounds, building.
We knew there was a risk that our competitors would take what makes us valuable, but we also knew that the bigger risk would have been to stop innovating.
So we took the leap and pivoted in one of the most exciting ways a technology company can go.
Seldon started as a content recommendation engine.
Seldon is tried and tested as a closed API in demanding enterprise environment serving billions of recommendations every month, mostly recommending articles on news websites.
In 2014 we took the business in a new open-source direction to create Seldon.
2016 – we’re building out a fintech product and are aiming to establish a POC with Barclays. From early discussions there are many parts of the bank that can benefit from Seldon. Joke about Risk Weighted Asset.
There are a number of interconnected market forces at work that means 2016 will be a tipping point for machine intelligence:
250 billion billion (250 x 10^18) transistors were produced in 2014. Every second of that year, on average, 8 trillion transistors were produced. That figure is about 25 times the number of stars in the Milky Way. (according to Moores law production should now have doubled.)
58% of job activities can be automated. 47% of jobs will be taken over cognitive machines in the next 10 years
2 billion smartphones;13 billion connected devices.
Seldon was one of the first companies to open source a machine learning platform.
But last year we saw Google open-source TensorFlow, IBM donated SystemML to Apache.
Elon Musk and Sam Altman form a non-profit AI research org called OpenAI.
Open-source is a huge benefit to enterprise, particularly banks where data privacy and compliance are particularly important, as it gives full control with an on premise deployment.
Seldon isn’t an OS library, it’s an end-to-end machine learning PIPELINE. Include best of Open Source and algorithms built ourselves.
1. CONNECT YOUR DATA - Ingesting behavioural data from events that contains metadata and context such as device and location.
2. MODEL BUILDING - Multiple models are built based on desired Goals (Could be a KPI or an action/event).
Behavioral data plus algorithms are used to train the predictive models. computationally inefficient to store all the possible alternatives… realtime behavioural data needs to update the models in real-time. value not in the algorithm, the value is in the model.
INDUSTRY MODEL - working in media, advertising and ecommerce.
3. OUTPUTS - there are currently two outputs for Seldon - one is a recommendation and another is a prediction (score).
FEEDBACK LOOP - Models are optimised in a recursive way…
FURTHER INFO
Seldon pulls in behavioral data from any digital environment, builds predictive models and outputs recommendations and predictions at SCALE.
But have built a generic platform with a broad range of applications including finance, insurance and healthcare.
Build in house: DEMAND - there are far fewer for truly skilled machine learning and AI developers than big data engineers. Improving internal data science capabilities is increasingly important for companies. So they are hiring or aquahiring teams of data scientists.
3rd party: DATA SECURITY - there are many companies with data control policies that require the hosting of consumer data behind their firewall, which a flexible open source solution will allow.
Sometimes there’s no transparency on algorithms.
Open Source. MARKET DISRUPTION Open source technologies such as Docker, Hadoop, and Apache Spark, have superseded proprietary operating systems and databases. Meanwhile, most vendors higher up the data science stack (i.e. providing predictive analytics, recommendations, and machine learning APIs) are effectively licensing black box solutions. Seldon wants to reduce barriers to entry and get the Seldon’s technology that we believe in the hands of as many developers as possible.
Businesses wanting to solve their own / domain-specific problems.
ADD PRIVACY….
WHY? Data scientists want more CONTROL to solve the problems specific to their business.
PARATOS LAW in action – people should be spending 90% of their time solving the 10% of the domain-specific problems that make the biggest impact on their business, but otherwise data scientists are focusing on the remaining 90% and wasting time on reinventing the wheel.
I’ll share with you a quick analogy with this DJ MIXER:
Each channel on the mixer represents a predictive model.
The controls represent hyper-parameters.
Data scientist is the DJ listening to the audience and adjusting the controls accordingly.
Seldon gives the DJ super powers. Enabling them to play all of the stages of a festival at the same time, so your audiences is not stuck listening to Lionel Richie (collaborative filtering) if you want to listen to Metallica (matrix factorization)
But remember, you can always tap the DJ on the shoulder and make special requests.
OPEN SOURCE
SETUP. SaaS platform grows roots, provisioning usually internal process with continuous integration and deployment. Rarely setup new infrastructure from scratch. VIRTUAL MACHINE.
DOCUMENTATION. SaaS businesses don’t need as much. Docs on Github so people can commit changes – first pull requests. Documentation gets the highest engagement to see how to use it. [show documentation]
CHOOSING A LICENSE. Reason for Apache 2 vs LGPL/GPL – better for business because they don’t have to make modifications open source.
SAAS – open source is a great driver of SaaS customers. Many companies want to start with SaaS and have a longer term plan to move on-prem and work with custom algos.
SALES CYCLE –
INBOUND: more leads via open-source. Previously contract before getting tech in hands of developers.
TRACKING – companies much further along the funnel because they don’t have to sign up to get started.
VALUE CHAIN – where we sit now we have disrupted ourselves.
TIMING (OS more strategic and sometimes SaaS is a better option).
COMMUNITY – newsletter, github, detail release notes, clean codebase, future: events, etc. Important for us.
- As SaaS: classically delivering endpoint
- In comparison as open source: enable looking into configuration --> make product & onboarding more streamlined
- Documentation important for activating developers --> transfer docs/pdf to Github
- Interestingly: first pull requests about fixing documentation
- Create demo apps to show possibilities
- Huge thing: community --> changed way of communication
- Inbound instead of outbound
- License issues --> solved with Apache 2 license
- Sales cycle got longer
- Didn’t lose any customers through going open
- Found that open source is a good distribution channel
- Find right business/revenue model for open source
- Deployment --> cloud vs. on premise
- Build ecosystem around proactive community & potentially work together with competitors
- Architecture enables microservices & API --> interfaces with other ML services
- Integration with other open source libraries & closed APIs
- Change in cost structure
- Conflict in support (free vs. SaaS)?
- Measuring open source engagement?
There are INFINITE algorithm configurations to choose from. So which ones are best for my business?
Example of when different models used: high/low churn, days of week. Userbase or product mix changes. Seasonal changes.
YOU CAN USE YOUR OWN ALGORITHMS AND MODELS.
The model selection will vary depending on the user type.
OLD WAY - AB testing. each test used to be a manual process measure the impact of recommendations on KPIs such as CTR, conversion rate, etc..
NEW WAY – CONTINUOUS TESTING of all models that diverts more traffic towards the model performing best at that specific point in time. Called a MULTI-ARMED BANDIT, inspired by a strategy to play a room full of slot machines.
Give a user case of which algorithms would be used high churn news environment. You want to make the case that it’s not 1 algorithm/model but a combination of different models that will maximise your KPI.
And that’s why you need Seldon. We A/B test from set of INDUSTRY MODELS to find a combination of model selections that work best for your business for a given user at a given time. TIME SAVED in choosing the best model. Seldon increases the productivity of your data science teams and helps your business to increase profits through rapid prototyping and better KPI performance.
Recommendations using EXTERNAL REST API
Predictions takes JSON of /events data and provides regression and classification outputs.
Microservices /predictions with Vowpal Wabbit
Example /recommendations microservices – including Collaborative Deep Learning from the KDD talk.
Example of community member using Microservices to test various matrix factorization implementation.
Two dimensions: horizontal vs vertical (market focus) and product (scale) vs service (consulting)
After open-source Seldon in the bottom right, providing services on top of a horizontal platform. ”Integration Services” or “Customer Funded Development”
Companies generally seek a position in the top right, unicorn territory. But it doesn’t make sense for us to jump there directly.
So we’re first making it much easier to deploy Seldon’s product through SaaS and optimising setup on our horizontal platform and carefully picking some areas to focus on.
Our aim is to spawn many unicorns.
https://thenounproject.com/term/mule/28242/
MICROSERVICES – make the Seldon stack completely pluggable with third party code / algos developed by your data scientists IN ANY LANGUAGE (R, Python, etc) that can be put into production and utilise the same pipeline as core Seldon algorithms.
OPEN SOURCE LIBRARIES – we already used microservices to connect an OS library called Vowpal Wabbit (Microsoft research) to power our new predict endpoint. This enables regression, binary and multi-class classification. We are connected to the leading machine intelligence libraries such as Torch.
CLOSED APIs – third parties who provide We can leverage third party APIs such as IBM Watson for personality insights or text to speech.
ENTERPRISE DISTRIBUTIONS – Seldon are already planning integrations with some of the leading enterprise distributions.
4. SELDON CONTAINER INFRASTRUCTURE
MAIN COMPONENTS – REST API SERVERS, ZOOKEEPER, FLUENTD, KAFKA, SPARK, MEMCACHE, JDBC database…
VIRTUAL MACHINE - Setting up infrastructure is complex. Portable - Developer can download it and straight away access the infrastructure. Movie demo.
DOCKER / AMIs - technology that allows you to use on different platforms (docker container = shipping container. Part of our infrastructure in each container). Deploy on premise, cloud (AWS, Google) or SaaS. [encourage people to register for AMIs]
WORK IN PROGRESS
Flexible design to allow different techniques to attack the problem
Stream based - Apache Kafka as hub
data pushed to DBs and processing units as needed
Stream algorithms via Flink (or Spark) dependent on latency requirements
Batch algorithms via Spark (or Flink)
Low latency front end scoring systems
evolution of Seldon server with likely input from trading systems expertise
Zookeeper for state and control, Luigi pipeline
Docker Swarm for deployment
Python single machine for agile algorithm development
scikit-learn, pandas, pyseldon etc.
Data scientists and SALES
Come and work at Barclays Accelerator
BEFORE TAKING SOME QUESTIONS, I’LL FINISH WITH A QUESTION.
Can we change the way in which people view their competition? London is become a centre of excellence for AI. And companies that collaborate as part of an ecosystems have a competitive advantage.
Since Seldon went OS had an open door policy about speaking to “competitors”.
Download Seldon’s open source, VM or AMI... visit seldon.io or develors head on over to docs.seldon.io
Contact me if you would like to help out or to discuss how we can help add machine intelligence into your business. Or other AI companies that want to partner.
THANK YOU
(quite often this is the elephant in the room – it’s often the first question we get asked)
OS is revolutionising the way in which we do business. Since we released Seldon in Feb it is already starting to be used by world’s largest companies.
FREE - First, don’t expect more than a small percentage of activated users to pay.
ADVISORY. ML is complicated and more projects have unique requirements. Advisory services, enables us to spend more time with customers, understanding their problems so ultimately leads to building a better platform.
MLAAS. Finally, we have a SaaS platform! Many companies find us via the OS but SaaS the best starting point. Companies like the MIGRATION PATH [can reference later in call for open models]
ECOSYSTEM. Third party technology, APIs and models. Also UNIFY AND MONETISE the DATA. Value in creating an ecosystem. Seldon can offer distribution and monetization.