Machine learning has become an important tool in the modern software toolbox, and high-performing organizations are increasingly coming to rely on data science and machine learning as a core part of their business. eBay introduced machine learning to its commerce search ranking and drove double-digit increases in revenue. Stitch Fix built a multibillion dollar clothing retail business in the US by combining the best of machines with the best of humans. And WeWork is bringing machine-learned approaches to the physical office environment all around the world. In all cases, algorithmic techniques started simple and slowly became more sophisticated over time. This talk will use these examples to derive an agile approach to machine learning, and will explore that approach across several different dimensions. We will set the stage by outlining the kinds of problems that are most amenable to machine-learned approaches as well as describing some important prerequisites, including investments in data quality, a robust data pipeline, and experimental discipline. Next, we will choose the right (algorithmic) tool for the right job, and suggest how to incrementally evolve the algorithmic approaches we bring to bear. Most fancy cutting-edge recommender systems in the real world, for example, started out with simple rules-based techniques or basic regression. Finally, we will integrate machine learning into the broader product development process, and see how it can help us to accelerate business results
Faster is Better. High-performing organizations deploy both substantially faster and substantially more reliably, and thus are 2.5x more likely to achieve business goals. This keynote covers how to move fast at large scale:
* Organizing for Speed
* What to Build and What NOT to Build
* When to Build
* How to Build
* Delivering and Operating
Keynote at Reversim Summit 2017 in Tel Aviv, Israel.
Minimum Viable Architecture - Good Enough is Good EnoughRandy Shoup
The “right” architecture and organization depends on the size and scale of your company. The only constant is change, and what works for 5 engineers does not work for 5000. Based upon lessons from Google and eBay, learn how to evolve both technology and organization together successfully.
This presentation is based on many hard-won lessons by the speaker, who led large-scale engineering teams at Google and eBay, but also co-founded a tiny startup and tried (unsuccessfully) to apply the same techniques. This session hopes to help others from making the same mistakes by introducing the concept of “Minimal Viable Architecture”. It outlines the common architectural evolution of a company or project through the search, execution, and scaling phases, and discusses the appropriate technologies, disciplines, and organizational structures at each phase. You'll start with a monolith, and end up with microservices, and that's completely and entirely appropriate.
As the research in Accelerate and in the DevOps Handbook shows, high-performing organizations deliver more rapidly, more repeatably, and more reliably. And as an organization scales, it becomes more and more important to get the product development process right. Drawing on the speaker's experiences leading high-performing organizations at Google and eBay, this session discusses the upstream parts of that process, focusing on organization, problem definition, and prioritization. We will discuss forming small, cross-functional teams with clear areas of responsibility. Then we will discuss the importance of clearly defining the problem we are trying to solve as a team. Finally, we will cover focus and prioritization -- how we decide what to do when. You will take away actionable techniques you can apply in your own organization.
DevOps is far more about culture and organization than it is about technology and tooling. This talk will discuss the speaker's experiences leading high-performing engineering teams at Google, eBay, and Stitch Fix, and will offer suggestions for other organizations to level up their DevOps game.
https://www.meetup.com/SV-ELC/events/240087808/
Modern software-service models take advantage of the great benefits in having the same team both build the software as well as operate it in production -- "You Build It; You Run It" is the Amazon mantra. What does this mean in practice?
Organizationally, it means small teams with well-defined areas of responsibility, directly aligned with the business. The teams are cross-functional, meaning that each team has all the skill sets it requires to do its job, while at the same time relying on other teams for supporting services, tools, and libraries.
Process-wise, it means doubling down on practices like test-driven development and continuous delivery. Using continuous delivery practices, high-performing teams can and do release their applications and services multiple times a day. This enables them to iterate rapidly, experiment courageously, and fail more quickly.
Culturally, it means end-to-end ownership. Each team owns its software end-to-end, from design to development to deployment to retirement. The same engineers who are responsible for the features are responsible for quality, performance, operations, and maintenance. This ownership puts incentives in the right place to encourage building maintainable, observable, and operable systems from the start.
All these techniques and approaches are available to everyone, and practical examples in this talk will help other organizations on their journey.
Service Architectures At Scale - QCon London 2015Randy Shoup
Over time, almost all large, well-known web sites have evolved their architectures from an early monolithic application to a loosely-coupled ecosystem of polyglot microservices. While first-order goals are almost always driven by the needs of scalability and velocity, this evolution also produces second-order effects on the organization as well. This session will discuss modern service architectures at scale, using specific examples from both Google and eBay.
It covers some interesting -- and perhaps nonintuitive -- lessons learned in building and operating these sites. It concludes with a number of experience-based recommendations for other smaller organizations evolving to -- and sustaining -- an effective service ecosystem.
Scaling Your Architecture for the Long TermRandy Shoup
This talk from the virtual 2020 CTO Summit (https://www.ctoconnection.com/summits) covers several architecture lessons to help you survive and thrive through the scaling phase of your company:
* Modular Architecture
* Event-Driven Communication
* Quality and Reliability
* Continuous Delivery
Minimum Viable Architecture -- Good Enough is Good Enough in a StartupRandy Shoup
I have spent the last decade building large-scale systems at eBay and Google -- and talking publicly about it -- and this presentation is about why a startup should completely ignore what I said! In an early-stage startup, it is not only not worth architecting for a future of massive scale; it is actively counterproductive. This presentation from the SF Startup CTO Summit outlines the common architectural evolution of a startup through the search, execution, and scaling phases, and discusses the appropriate technologies and disciplines at each phase. It ends with some real-world examples from eBay, Twitter, and Amazon to illustrate the point.
How do effective large-scale service ecosystems work? Keynote Presentation at Istanbul Tech Talks 2018
How to Design Services
* Systems of record
* Interface specification
* Interface backward / forward compatibility
Service Ecosystems
* Layered services
* "Standardization" through encouragement
* Vendor-customer relationships between teams
Operating and Deploying Services
* Data Migration
* Automated Pipelines
* Incremental Deployment
* Feature Flags
Faster is Better. High-performing organizations deploy both substantially faster and substantially more reliably, and thus are 2.5x more likely to achieve business goals. This keynote covers how to move fast at large scale:
* Organizing for Speed
* What to Build and What NOT to Build
* When to Build
* How to Build
* Delivering and Operating
Keynote at Reversim Summit 2017 in Tel Aviv, Israel.
Minimum Viable Architecture - Good Enough is Good EnoughRandy Shoup
The “right” architecture and organization depends on the size and scale of your company. The only constant is change, and what works for 5 engineers does not work for 5000. Based upon lessons from Google and eBay, learn how to evolve both technology and organization together successfully.
This presentation is based on many hard-won lessons by the speaker, who led large-scale engineering teams at Google and eBay, but also co-founded a tiny startup and tried (unsuccessfully) to apply the same techniques. This session hopes to help others from making the same mistakes by introducing the concept of “Minimal Viable Architecture”. It outlines the common architectural evolution of a company or project through the search, execution, and scaling phases, and discusses the appropriate technologies, disciplines, and organizational structures at each phase. You'll start with a monolith, and end up with microservices, and that's completely and entirely appropriate.
As the research in Accelerate and in the DevOps Handbook shows, high-performing organizations deliver more rapidly, more repeatably, and more reliably. And as an organization scales, it becomes more and more important to get the product development process right. Drawing on the speaker's experiences leading high-performing organizations at Google and eBay, this session discusses the upstream parts of that process, focusing on organization, problem definition, and prioritization. We will discuss forming small, cross-functional teams with clear areas of responsibility. Then we will discuss the importance of clearly defining the problem we are trying to solve as a team. Finally, we will cover focus and prioritization -- how we decide what to do when. You will take away actionable techniques you can apply in your own organization.
DevOps is far more about culture and organization than it is about technology and tooling. This talk will discuss the speaker's experiences leading high-performing engineering teams at Google, eBay, and Stitch Fix, and will offer suggestions for other organizations to level up their DevOps game.
https://www.meetup.com/SV-ELC/events/240087808/
Modern software-service models take advantage of the great benefits in having the same team both build the software as well as operate it in production -- "You Build It; You Run It" is the Amazon mantra. What does this mean in practice?
Organizationally, it means small teams with well-defined areas of responsibility, directly aligned with the business. The teams are cross-functional, meaning that each team has all the skill sets it requires to do its job, while at the same time relying on other teams for supporting services, tools, and libraries.
Process-wise, it means doubling down on practices like test-driven development and continuous delivery. Using continuous delivery practices, high-performing teams can and do release their applications and services multiple times a day. This enables them to iterate rapidly, experiment courageously, and fail more quickly.
Culturally, it means end-to-end ownership. Each team owns its software end-to-end, from design to development to deployment to retirement. The same engineers who are responsible for the features are responsible for quality, performance, operations, and maintenance. This ownership puts incentives in the right place to encourage building maintainable, observable, and operable systems from the start.
All these techniques and approaches are available to everyone, and practical examples in this talk will help other organizations on their journey.
Service Architectures At Scale - QCon London 2015Randy Shoup
Over time, almost all large, well-known web sites have evolved their architectures from an early monolithic application to a loosely-coupled ecosystem of polyglot microservices. While first-order goals are almost always driven by the needs of scalability and velocity, this evolution also produces second-order effects on the organization as well. This session will discuss modern service architectures at scale, using specific examples from both Google and eBay.
It covers some interesting -- and perhaps nonintuitive -- lessons learned in building and operating these sites. It concludes with a number of experience-based recommendations for other smaller organizations evolving to -- and sustaining -- an effective service ecosystem.
Scaling Your Architecture for the Long TermRandy Shoup
This talk from the virtual 2020 CTO Summit (https://www.ctoconnection.com/summits) covers several architecture lessons to help you survive and thrive through the scaling phase of your company:
* Modular Architecture
* Event-Driven Communication
* Quality and Reliability
* Continuous Delivery
Minimum Viable Architecture -- Good Enough is Good Enough in a StartupRandy Shoup
I have spent the last decade building large-scale systems at eBay and Google -- and talking publicly about it -- and this presentation is about why a startup should completely ignore what I said! In an early-stage startup, it is not only not worth architecting for a future of massive scale; it is actively counterproductive. This presentation from the SF Startup CTO Summit outlines the common architectural evolution of a startup through the search, execution, and scaling phases, and discusses the appropriate technologies and disciplines at each phase. It ends with some real-world examples from eBay, Twitter, and Amazon to illustrate the point.
How do effective large-scale service ecosystems work? Keynote Presentation at Istanbul Tech Talks 2018
How to Design Services
* Systems of record
* Interface specification
* Interface backward / forward compatibility
Service Ecosystems
* Layered services
* "Standardization" through encouragement
* Vendor-customer relationships between teams
Operating and Deploying Services
* Data Migration
* Automated Pipelines
* Incremental Deployment
* Feature Flags
DOES15 - Randy Shoup - Ten (Hard-Won) Lessons of the DevOps TransitionGene Kim
Randy Shoup, Consulting CTO
DevOps is no longer just for Internet unicorns any more. Today many large enterprises are transitioning from the slow and siloed traditional IT approach to modern DevOps practices, and getting substantial improvements in agility, velocity, scalability, and efficiency. But this transition is not without its challenges and pitfalls, and those of us who have led this journey have the scar tissue to prove it.
A successful transition to DevOps practices ultimately involves changes to organization, to culture, and to architecture. Organizationally, we want to create multi-skilled teams with end-to-end ownership and shared on-call responsibilities. Culturally, we want to prioritize solving problems and improving the product over closing tickets. Architecturally, we want to move to an infrastructure with independently testable and deployable components.
The ten practical lessons outlined in this session synthesize the speaker’s experiences leading teams at eBay, Google, and KIXEYE, as well as from his current consulting practice.
Why Enterprises Are Embracing the CloudRandy Shoup
After being deeply involved in public cloud for the last several years, as both a provider and a consumer, I have been very pleasantly surprised at the rate at which large enterprises are rapidly moving to the cloud. For all the right reasons, even the most regulated and risk-averse of industries -- banking, for example -- are rapidly moving workloads out of their own owned data centers. Public cloud is not just for the "unicorns", but for the "horses" as well. This short vignette, presented at the GOTO Aarhus 2014 conference, tries to explain why this trend will continue and accelerate, and why we should be excited about it.
This presentation introduces the idea of a "Minimal Viable Architecture". As a company and product evolves, its architecture should evolve as well. We talk about the different phases of a product -- from the idea phase, to the starting phase, scaling phase, and optimizing phase. For each phase, we discuss the goals and constraints on the business, and we suggest an appropriate software architecture to match. Throughout the presentation, we use examples from eBay, Google, StitchFix, and others.
Evolving Architecture and Organization - Lessons from Google and eBayRandy Shoup
Keynote at DevOpsDays Cuba
Successful Internet companies are built on a foundation of excellent culture, efficient organization, and solid technology. As a company needs to scale, all of these parts of the foundation need to grow and scale with it. This session covers modern best practices at innovative companies in Silicon Valley for scaling culture, organization, and technology. Driven primarily by the presenter's experience ranging from small Valley startups to Google and eBay, it discusses:
* Organizing small, fast-moving engineering teams
* Building a scalable system out of smaller microservices
* Maintaining a culture of ownership and collaboration
* Developing effective engineering processes of continuous integration and continuous delivery
What if we designed our organizations like we design our systems? Applying scalability principles that we know from building large-scale distributed systems, as well as practical lessons learned at eBay and Google, this session covers how we can design and evolve our engineering organizations to scale.
One of the most powerful trends in software today is building large systems out of composable microservices. Many large-scale web companies have migrated over time to this architecture – and for good reason. But, as with any powerful technique, microservices come with their own brand of tradeoffs, and it is important to be aware of them before deciding whether they are appropriate in any particular case. They are not for every scale of problem, for every stage of company, or for every team.
This session takes a pragmatic approach to microservices, and compares them to the alternatives at different stages of company evolution. Using examples both from Google and eBay as well as from smaller organizations, it makes practical suggestions about whether, when, and how an organization should consider adopting a microservices architecture. Assuming microservices are the appropriate choice, it outlines an experience-based, incremental approach to making a successful rearchitecture to microservices.
One Terrible Day at Google, and How It Made Us BetterRandy Shoup
In October 2012, Google App Engine had an 8-hour global outage. This session walks through the incident and the "Reliability Fixit" it inspired in its aftermath. Learn how the team came together, and over the next 6 months, reduced reliability issues by 10x. Also take away broader insights around engineering tradeoffs, managing an incident, and driving improvement.
DevOpsDays Silicon Valley 2014 - The Game of OperationsRandy Shoup
Operating online games is fun and challenging. Games are some of the spikiest workloads around, and real-time really means *real-time*. Randy shares many of the DevOps techniques his team has put into practice at KIXEYE: Cloud infrastructure, Service teams, and DevOps Culture. He talks about elastic workloads, micro-services, configuration automation, and a common service "chassis". He further discusses the organizational and technical disciplines of team autonomy, internal vendor-customer relationships, and, of course, "you build it, you run it"!
Scaling Your Architecture with Services and EventsRandy Shoup
This session is a deep dive into the modern best practices around asynchronous decoupling, resilience, and scalability that allow us to implement a large-scale software system from the building blocks of events and services, based on the speaker's experiences implementing such systems at Google, eBay, and other high-performing technology organizations. We will outline the various options for handling event delivery and event ordering in a distributed system. We will cover data and persistence in an event-driven architecture. Finally, we will describe how to combine events, services, and so-called 'serverless' functions into a powerful overall architecture. You will leave with practical suggestions to help you accelerate your development velocity and drive business results.
The Importance of Culture: Building and Sustaining Effective Engineering Org...Randy Shoup
Randy is a 25-year veteran of Silicon Valley, having led engineering organizations at eBay, Google, Oracle, and a number of other companies. Through the lens of his personal experience from hands-on engineer to architect to CTO, at organizations ranging from tiny startups to global giants, Randy will discuss several important aspects of engineering cultures, which both support and hinder the ability to innovate: hiring and retention, ownership and collaboration, quality and discipline, and learning and experimentation.
Randy will suggest some learnings about what has worked well -- and what has not -- in creating and sustaining an effective engineering culture. He will further offer some concrete suggestions on how other organizations -- both large and small -- can evolve their cultures as well.
Learning from Learnings: Anatomy of Three IncidentsRandy Shoup
The best response to a system outage is not "What did you do?", but "What did we learn?" This session will walk through three system-wide outages at Google, at Stitch Fix, and at WeWork—their incidents, aftermaths, and recoveries. In all cases, many things went right and a few went wrong; also in all cases, because of blameless cultures, we buckled down, learned a lot, and made substantial improvements in the systems for the future. Looking back with the perspective of 20-20 hindsight, all of these incidents were seminal events that changed the focus and trajectory of engineering at each organization. You will leave with a set of actionable suggestions in dealing with customers, engineering teams, and upper management. You will also enjoy a few war stories from the trenches.
Managing Data at Scale - Microservices and EventsRandy Shoup
An ambitious attempt at BuildStuff España 2018 to cover, in 50 minutes:
* Migrating to Microservices
* Challenges of Data in Microservices (including shared data, joins, and transactions)
* Challenges of Event-Driven Systems (including event duplication and event ordering)
Anatomy of Three Incidents -- Commonalities and LessonsRandy Shoup
The best response to a system outage is not "What did you do?", but "What did we learn?" This session will walk through three system-wide outages at Google, at Stitch Fix, and at WeWork—their incidents, aftermaths, and recoveries. In all cases, many things went right and a few went wrong; also in all cases, because of blameless cultures, we buckled down, learned a lot, and made substantial improvements in the systems for the future. Looking back with the perspective of 20-20 hindsight, all of these incidents were seminal events that changed the focus and trajectory of engineering at each organization. You will leave with a set of actionable suggestions in dealing with customers, engineering teams, and upper management. You will also enjoy a few war stories from the trenches.
Flowcon2013 - Virtuous Cycles of Velocity: What I Learned About Going Fast at...Randy Shoup
eBay and Google operate some of the largest Internet sites on the planet, and each maintains its leadership through continuous innovation in infrastructure and products. While substantially different in their detailed approaches, both organizations sustain their feature velocity through a combination of People, Technology, and Culture. This session explores how these large-scale sites do it, what works well and what could be done better. It offers some concrete suggestions on how other organizations -- both large and small -- can do the same.
Teaching Machines to Fish -- How eBay Improves ItselfRandy Shoup
eBay Distinguished Architect Randy Shoup describes eBay's use of machine learning and classification techniques to continually improve the quality of its search results and its overall site experience
Yetizen (https://www.linkedin.com/company/yetizen/about/) was a gaming incubator that existed in San Francisco, roughly between 2011 and 2015. I thought it was an interesting experiment, and was happy to give a series of talks there, and advise the portfolio companies.
This talk, from 2013, is about what's involved in being a platform vendor-- a third party whose service is relied up by applications. From the fact that your customers (application companies) don't really trust you to the fact that they make unreasonable demands to the fact that platforms and services are architected differently from applications; it's all in here.
Serverless Architectures enable scalable and cost-effective apps to be built faster, so they can dramatically increase the odds of Your Startup's Success!
In "Startups + Serverless = Match made in Heaven" meetup, www.ServerlessToronto.org members discussed how to help Entrepreneurs push their businesses up to "other side of the teeterboard" (without failing) using the Serverless technologies: https://www.youtube.com/watch?v=1SqfJo47kMA
Velocity Conference NYC 2014 - Real World DevOpsRodrigo Campos
In a world where agility has become a requirement, business and engineering demands have decreed the death of the “Department of No”. This talk will cover the journey of an IT Operations department from a single DevOps team to a business-wide cultural shift that has affected the way people interact and work with each other.
In order to make sure that our DevOps initiative would be successful, we needed to make changes to the corporate organization, rearrange teams and roles in several areas, and make sure that everyone fully understand where we were being headed to.
All these steps will be covered in this talk that will demonstrate some common pitfalls and misconceptions that jeopardize the DevOps adoption, particularly in large enterprises with several compliancy requirements and some outdated bureaucracy.
2015 Mastering SAP Tech - Enterprise Mobility - Testing Lessons LearnedEneko Jon Bilbao
Lessons learned from a large enterprise mobility roll-out project for an Asset Management and maintenance workforce. Managing User expectations, testing tools, gotcha areas and what we did about them. Presentation from Mastering SAP Technology conference 2015.
My talk from Drupalcamp London Business Day on 1st March 2013
When building big websites, you're going to face a lot of problems regardless of your technology choice. This talk unveils some of the common problems, and shows how the Drupal community will help you solve these problems.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Mistakes we make_and_howto_avoid_them_v0.12Trevor Warren
This presentation was put together for the CMGA (www.cmga.org.au) meetup in Canberra (ACT), Australia. It's an attempt to share some of my experiences building and delivering systems over the last decade and a half.
DOES15 - Randy Shoup - Ten (Hard-Won) Lessons of the DevOps TransitionGene Kim
Randy Shoup, Consulting CTO
DevOps is no longer just for Internet unicorns any more. Today many large enterprises are transitioning from the slow and siloed traditional IT approach to modern DevOps practices, and getting substantial improvements in agility, velocity, scalability, and efficiency. But this transition is not without its challenges and pitfalls, and those of us who have led this journey have the scar tissue to prove it.
A successful transition to DevOps practices ultimately involves changes to organization, to culture, and to architecture. Organizationally, we want to create multi-skilled teams with end-to-end ownership and shared on-call responsibilities. Culturally, we want to prioritize solving problems and improving the product over closing tickets. Architecturally, we want to move to an infrastructure with independently testable and deployable components.
The ten practical lessons outlined in this session synthesize the speaker’s experiences leading teams at eBay, Google, and KIXEYE, as well as from his current consulting practice.
Why Enterprises Are Embracing the CloudRandy Shoup
After being deeply involved in public cloud for the last several years, as both a provider and a consumer, I have been very pleasantly surprised at the rate at which large enterprises are rapidly moving to the cloud. For all the right reasons, even the most regulated and risk-averse of industries -- banking, for example -- are rapidly moving workloads out of their own owned data centers. Public cloud is not just for the "unicorns", but for the "horses" as well. This short vignette, presented at the GOTO Aarhus 2014 conference, tries to explain why this trend will continue and accelerate, and why we should be excited about it.
This presentation introduces the idea of a "Minimal Viable Architecture". As a company and product evolves, its architecture should evolve as well. We talk about the different phases of a product -- from the idea phase, to the starting phase, scaling phase, and optimizing phase. For each phase, we discuss the goals and constraints on the business, and we suggest an appropriate software architecture to match. Throughout the presentation, we use examples from eBay, Google, StitchFix, and others.
Evolving Architecture and Organization - Lessons from Google and eBayRandy Shoup
Keynote at DevOpsDays Cuba
Successful Internet companies are built on a foundation of excellent culture, efficient organization, and solid technology. As a company needs to scale, all of these parts of the foundation need to grow and scale with it. This session covers modern best practices at innovative companies in Silicon Valley for scaling culture, organization, and technology. Driven primarily by the presenter's experience ranging from small Valley startups to Google and eBay, it discusses:
* Organizing small, fast-moving engineering teams
* Building a scalable system out of smaller microservices
* Maintaining a culture of ownership and collaboration
* Developing effective engineering processes of continuous integration and continuous delivery
What if we designed our organizations like we design our systems? Applying scalability principles that we know from building large-scale distributed systems, as well as practical lessons learned at eBay and Google, this session covers how we can design and evolve our engineering organizations to scale.
One of the most powerful trends in software today is building large systems out of composable microservices. Many large-scale web companies have migrated over time to this architecture – and for good reason. But, as with any powerful technique, microservices come with their own brand of tradeoffs, and it is important to be aware of them before deciding whether they are appropriate in any particular case. They are not for every scale of problem, for every stage of company, or for every team.
This session takes a pragmatic approach to microservices, and compares them to the alternatives at different stages of company evolution. Using examples both from Google and eBay as well as from smaller organizations, it makes practical suggestions about whether, when, and how an organization should consider adopting a microservices architecture. Assuming microservices are the appropriate choice, it outlines an experience-based, incremental approach to making a successful rearchitecture to microservices.
One Terrible Day at Google, and How It Made Us BetterRandy Shoup
In October 2012, Google App Engine had an 8-hour global outage. This session walks through the incident and the "Reliability Fixit" it inspired in its aftermath. Learn how the team came together, and over the next 6 months, reduced reliability issues by 10x. Also take away broader insights around engineering tradeoffs, managing an incident, and driving improvement.
DevOpsDays Silicon Valley 2014 - The Game of OperationsRandy Shoup
Operating online games is fun and challenging. Games are some of the spikiest workloads around, and real-time really means *real-time*. Randy shares many of the DevOps techniques his team has put into practice at KIXEYE: Cloud infrastructure, Service teams, and DevOps Culture. He talks about elastic workloads, micro-services, configuration automation, and a common service "chassis". He further discusses the organizational and technical disciplines of team autonomy, internal vendor-customer relationships, and, of course, "you build it, you run it"!
Scaling Your Architecture with Services and EventsRandy Shoup
This session is a deep dive into the modern best practices around asynchronous decoupling, resilience, and scalability that allow us to implement a large-scale software system from the building blocks of events and services, based on the speaker's experiences implementing such systems at Google, eBay, and other high-performing technology organizations. We will outline the various options for handling event delivery and event ordering in a distributed system. We will cover data and persistence in an event-driven architecture. Finally, we will describe how to combine events, services, and so-called 'serverless' functions into a powerful overall architecture. You will leave with practical suggestions to help you accelerate your development velocity and drive business results.
The Importance of Culture: Building and Sustaining Effective Engineering Org...Randy Shoup
Randy is a 25-year veteran of Silicon Valley, having led engineering organizations at eBay, Google, Oracle, and a number of other companies. Through the lens of his personal experience from hands-on engineer to architect to CTO, at organizations ranging from tiny startups to global giants, Randy will discuss several important aspects of engineering cultures, which both support and hinder the ability to innovate: hiring and retention, ownership and collaboration, quality and discipline, and learning and experimentation.
Randy will suggest some learnings about what has worked well -- and what has not -- in creating and sustaining an effective engineering culture. He will further offer some concrete suggestions on how other organizations -- both large and small -- can evolve their cultures as well.
Learning from Learnings: Anatomy of Three IncidentsRandy Shoup
The best response to a system outage is not "What did you do?", but "What did we learn?" This session will walk through three system-wide outages at Google, at Stitch Fix, and at WeWork—their incidents, aftermaths, and recoveries. In all cases, many things went right and a few went wrong; also in all cases, because of blameless cultures, we buckled down, learned a lot, and made substantial improvements in the systems for the future. Looking back with the perspective of 20-20 hindsight, all of these incidents were seminal events that changed the focus and trajectory of engineering at each organization. You will leave with a set of actionable suggestions in dealing with customers, engineering teams, and upper management. You will also enjoy a few war stories from the trenches.
Managing Data at Scale - Microservices and EventsRandy Shoup
An ambitious attempt at BuildStuff España 2018 to cover, in 50 minutes:
* Migrating to Microservices
* Challenges of Data in Microservices (including shared data, joins, and transactions)
* Challenges of Event-Driven Systems (including event duplication and event ordering)
Anatomy of Three Incidents -- Commonalities and LessonsRandy Shoup
The best response to a system outage is not "What did you do?", but "What did we learn?" This session will walk through three system-wide outages at Google, at Stitch Fix, and at WeWork—their incidents, aftermaths, and recoveries. In all cases, many things went right and a few went wrong; also in all cases, because of blameless cultures, we buckled down, learned a lot, and made substantial improvements in the systems for the future. Looking back with the perspective of 20-20 hindsight, all of these incidents were seminal events that changed the focus and trajectory of engineering at each organization. You will leave with a set of actionable suggestions in dealing with customers, engineering teams, and upper management. You will also enjoy a few war stories from the trenches.
Flowcon2013 - Virtuous Cycles of Velocity: What I Learned About Going Fast at...Randy Shoup
eBay and Google operate some of the largest Internet sites on the planet, and each maintains its leadership through continuous innovation in infrastructure and products. While substantially different in their detailed approaches, both organizations sustain their feature velocity through a combination of People, Technology, and Culture. This session explores how these large-scale sites do it, what works well and what could be done better. It offers some concrete suggestions on how other organizations -- both large and small -- can do the same.
Teaching Machines to Fish -- How eBay Improves ItselfRandy Shoup
eBay Distinguished Architect Randy Shoup describes eBay's use of machine learning and classification techniques to continually improve the quality of its search results and its overall site experience
Yetizen (https://www.linkedin.com/company/yetizen/about/) was a gaming incubator that existed in San Francisco, roughly between 2011 and 2015. I thought it was an interesting experiment, and was happy to give a series of talks there, and advise the portfolio companies.
This talk, from 2013, is about what's involved in being a platform vendor-- a third party whose service is relied up by applications. From the fact that your customers (application companies) don't really trust you to the fact that they make unreasonable demands to the fact that platforms and services are architected differently from applications; it's all in here.
Serverless Architectures enable scalable and cost-effective apps to be built faster, so they can dramatically increase the odds of Your Startup's Success!
In "Startups + Serverless = Match made in Heaven" meetup, www.ServerlessToronto.org members discussed how to help Entrepreneurs push their businesses up to "other side of the teeterboard" (without failing) using the Serverless technologies: https://www.youtube.com/watch?v=1SqfJo47kMA
Velocity Conference NYC 2014 - Real World DevOpsRodrigo Campos
In a world where agility has become a requirement, business and engineering demands have decreed the death of the “Department of No”. This talk will cover the journey of an IT Operations department from a single DevOps team to a business-wide cultural shift that has affected the way people interact and work with each other.
In order to make sure that our DevOps initiative would be successful, we needed to make changes to the corporate organization, rearrange teams and roles in several areas, and make sure that everyone fully understand where we were being headed to.
All these steps will be covered in this talk that will demonstrate some common pitfalls and misconceptions that jeopardize the DevOps adoption, particularly in large enterprises with several compliancy requirements and some outdated bureaucracy.
2015 Mastering SAP Tech - Enterprise Mobility - Testing Lessons LearnedEneko Jon Bilbao
Lessons learned from a large enterprise mobility roll-out project for an Asset Management and maintenance workforce. Managing User expectations, testing tools, gotcha areas and what we did about them. Presentation from Mastering SAP Technology conference 2015.
My talk from Drupalcamp London Business Day on 1st March 2013
When building big websites, you're going to face a lot of problems regardless of your technology choice. This talk unveils some of the common problems, and shows how the Drupal community will help you solve these problems.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Mistakes we make_and_howto_avoid_them_v0.12Trevor Warren
This presentation was put together for the CMGA (www.cmga.org.au) meetup in Canberra (ACT), Australia. It's an attempt to share some of my experiences building and delivering systems over the last decade and a half.
Tom DeMarco states that “You can’t control what you can’t measure”, but how much can we change and control (with) what we measure? This talk investigates the opportunities and limits of data-driven software engineering, shows which opportunities lie ahead of us when we engage in mining and analyzing software engineering process data, but also highlights important factors that influence the success and adaptability of data-based improvement approaches.
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web TestingPerfecto by Perforce
The widespread adoption of test automation has created many challenges — for everything from development lifecycle integration to scripting strategy.
One pitfall of automation is that teams often rush to automate everything they can. This is the automation firehose.
However, just because a scenario CAN be automated does not mean it SHOULD be automated. For scenarios that should be automated, teams must adopt implementation plans to ensure tests are reliable and deriving value.
Join this webinar led by Perfecto’s Chief Evangelist, Eran Kinsbruner, along with Thomas Haver, Manager of Automation & Delivery. In this session, the audience will:
-Understand which test scenarios to automate.
-Learn how to maximize the benefits of automation.
-Receive a checklist to determine automation feasibility and ROI.
Did you know that you can develop awesome products with zero product specifications ? We have recently quantified the gains for a product we built using Lean Startup and MVP approach and were pleasantly surprised to find that we could quantify minimum 47% gain in time-to-market, 32% cost savings, 55% improvement in product quality and 40% gain in business value as compared to traditional product development methods.
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
R+Hadoop - Ask Bigger (and New) Questions and Get Better, Faster AnswersRevolution Analytics
The business cases for Hadoop can be made on the tremendous operational cost savings that it affords. But why stop there? The integration of R-powered analytics in Hadoop presents a totally new value proposition. Organizations can write R code and deploy it natively in Hadoop without data movement or the need to write their own MapReduce. Bringing R-powered predictive analytics into Hadoop will accelerate Hadoop’s value to organizations by allowing them to break through performance and scalability challenges and solve new analytic problems. Use all the data in Hadoop to discover more, grow more quickly, and operate more efficiently. Ask bigger questions. Ask new questions. Get better, faster results and share them.
FlorenceAI: Reinventing Data Science at HumanaDatabricks
Humana strives to help the communities we serve and our individual members achieve their best health – no small task in the past year! We had the opportunity to rethink our existing operations and reimagine what a collaborative ML platform for hundreds of data scientists might look like. The primary goal of our ML Platform, named FlorenceAI, is to automate and accelerate the delivery lifecycle of data science solutions at scale. In this presentation, we will walk through an end-to-end example of how to build a model at scale on FlorenceAI and deploy it to production. Tools highlighted include Azure Databricks, MLFlow, AppInsights, and Azure Data Factory.
We will employ slides, notebooks and code snippets covering problem framing and design, initial feature selection, model design and experimentation, and a framework of centralized production code to streamline implementation. Hundreds of data scientists now use our feature store that has tens of thousands of features refreshed in daily and monthly cadences across several years of historical data. We already have dozens of models in production and also daily provide fresh insights for our Enterprise Clinical Operating Model. Each day, billions of rows of data are generated to give us timely information.
We already have examples of teams operating orders of magnitude faster and at a scale not within reach using fixed on-premise resources. Given rapid adoption from a dozen pilot users to over 100 MAU in the first 5 months, we will also share some anecodotes about key early wins created by the platform. We want FlorenceAI to enable Humana’s data scientists to focus their efforts where they add the most value so we can continue to deliver high-quality solutions that remain fresh, relevant and fair in an ever changing world.
Building an Open Source AppSec PipelineMatt Tesauro
Take the concepts of DevOps and apply them to AppSec and you have an AppSec Pipeline. Allow automation, orchestration and some ChatOps to expand the flow of your AppSec team since its not likely to get any bigger.
Performance doesn’t have the same definition between system administrators, developpers and business teams. What is Performance ? High CPU usage, not scalable web site, low business transaction rate per sec, slow response time, … This presentation is about maths, code performance, load testing, web performance, best practices, … Working on performance optimizaton is a very broad topic. It’s important to really understand main concepts and to have a clean and strong methodology because it could be a very time consumming activity. Happy reading !
New Model Testing: A New Test Process and ToolTEST Huddle
In this webinar, Paul described his experiences of building and using a bot for paired testing and also propose a new test process suitable for both high integrity and agile environments. His bot – codenamed System Surveyor – builds a model of the system as you explore and captures test ideas, risks and questions and generates structured test documentation as a by-product.
Outline of the generic process for an end-to-end data science project, beginning with definition of business requirements and ending with value-add brainstorming.
Alexander Podelko - Context-Driven Performance TestingNeotys_Partner
Since its beginning, the Performance Advisory Council aims to promote engagement between various experts from around the world, to create relevant, value-added content sharing between members. For Neotys, to strengthen our position as a thought leader in load & performance testing. During this event, 12 participants convened in Chamonix (France) exploring several topics on the minds of today’s performance tester such as DevOps, Shift Left/Right, Test Automation, Blockchain and Artificial Intelligence.
How Celtra Optimizes its Advertising Platformwith DatabricksGrega Kespret
Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions.
In this webinar, you will learn how Databricks helps Celtra to:
- Utilize Apache Spark to power their production analytics pipeline.
- Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics.
- Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.
Building and Scaling High Performing Technology Organizations by Jez Humble a...Agile India
High performing organizations don't trade off quality, throughput, and reliability: they work to improve all of these and use their software delivery capability to drive organizational performance. In this talk, Jez presents the results from DevOps Research and Assessment's five-year research program, including how continuous delivery and good architecture produce higher software delivery performance, and how to measure culture and its impact on IT and organizational culture. They explain the importance of knowing how (and what) to measure so you focus on what’s important and communicate progress to peers, leaders, and stakeholders. Great outcomes don’t realize themselves, after all, and having the right metrics gives us the data we need to keep getting better at building, delivering, and operating software systems.
More details:
https://confengine.com/agile-india-2019/proposal/8524/building-and-scaling-high-performing-technology-organizations
Conference link: https://2019.agileindia.org
Similar to An Agile Approach to Machine Learning (20)
Large Scale Architecture -- The Unreasonable Effectiveness of SimplicityRandy Shoup
Building distributed systems that work is hard. And scaling those systems by multiple orders of magnitude is even harder. Using examples from internet-scale consumer properties like Google, Amazon, and eBay, this talk deep-dives into the counterintuitive idea that the key to success in large-scale architecture is simplicity.
We first discuss simple components like modular services, orthogonal domain logic, and service layering. Next we discuss simple interactions between components, leveraging event-driven models, immutable logs, and asynchronous dataflow. Then we explore techniques that simplify making changes the system, including incremental changes, continuous testing, canary deployments, and feature flags.
In the final part of the talk, we show how all these ideas work together with specific architectural examples from Amazon, Netflix, and Walmart.
You will take away actionable insights you can immediately put into practice in your own systems.
Breaking Codes, Designing Jets, and Building TeamsRandy Shoup
Throughout engineering history, focused and empowered teams have consistently achieved the near-impossible. Alan Turing, Tommy Flowers, and their teams at Bletchley Park broke Nazi codes, saved their country, and brought down the Third Reich. Kelly Johnson and the Lockheed Skunk Works designed and built the XP-80 in 143 days, and later produced the U-2, the SR-71, and the F-22. Xerox PARC invented Smalltalk, graphical user interfaces, Ethernet, and the laser printer. What can this history teach us? Well, basically everything.
Effective teams have a purpose - a clearly defined problem which the entire team focuses on and owns end-to-end. Effective teams have an organizational culture that prioritizes collaboration and learning. And most importantly, effective teams are made up of people from diverse backgrounds and experiences.
If this sounds a lot like DevOps, or true little-a agile, that's no coincidence. But too few organizations actually practice these three-quarter-century-old ideas despite the overwhelming evidence that they work. So let's relearn those history lessons.
Monoliths, Migrations, and MicroservicesRandy Shoup
This talk describes several common challenges of software systems at scale:
* How to break up a monolithic application or a monolithic database into microservices.
* How to approach shared data, joins, and transactions in a microservices ecosystem
From the DevOps Enterprise Summit 2015, this presentation covers hard-won lessons of transitioning an engineering organization to DevOps. See video at https://www.youtube.com/watch?v=6tREbJl8e_Y.
Lessons:
1. Reorganize around Ownership
2. Lose the Ticket Culture
3. Replace Approvals with Code
4. Enforce a Service Mentality
5. Charge for Usage
6. Prioritize Quality
7. Start Investing in Testing
8. Actively Manage Technical Debt
9. Share On-Call Duties
10. Make Post-Mortems Truly Blameless
DevOps is no longer just for Internet unicorns any more. Today many large enterprises are transitioning from the slow and siloed traditional IT approach to modern DevOps practices, and getting substantial improvements in agility, velocity, scalability, and efficiency. But this transition is not without its challenges and pitfalls, and those of us who have led this journey have the scar tissue to prove it.
A successful transition to DevOps practices ultimately involves changes to organization, to culture, and to architecture. Organizationally, we want to create multi-skilled teams with end-to-end ownership and shared on-call responsibilities. Culturally, we want to prioritize solving problems and improving the product over closing tickets. Architecturally, we want to move to an infrastructure with independently testable and deployable components.
The ten practical lessons outlined in this session synthesize the speaker’s experiences leading teams at eBay, Google, and KIXEYE, as well as from his former consulting practice.
From a talk at the SF CTO Summit 2017 (https://www.ctoconnection.com/summits/sf2017), these slides cover the speaker's experience at Stitch Fix with managing data in a microservices environment. Areas include:
* Breaking up a monolithic database into services
* Using events as a first-class part of your architecture
* Sharing data among microservices
* Handling "joins" among microservices
* Simulating "transactions" among microservices using the Saga pattern
Effective Microservices In a Data-centric WorldRandy Shoup
From a talk at GOTOChicago 2017, these slides discuss the speaker's experiences at Stitch Fix with
* Organizational, Process, and Cultural prerequisites for being successful with Microservices: small teams, TDD / CD, DevOps
* How to handle shared data when your data is split among microservices
* How to handle "joins" across microservices
* How to simulate "transactions" across microservices
Slides link: https://gotochgo.com/3/sessions/79/slides
Video link: https://gotochgo.com/3/sessions/79/video
From the Monolith to Microservices - CraftConf 2015Randy Shoup
Most large-scale web companies have evolved their system architecture from a monolithic application and monolithic database to a set of loosely coupled microservices. Using examples from Google, eBay, and other large-scale sites, this talk outlines the pros and cons of these different stages of evolution, and makes practical suggestions about when and how other organizations should consider migrating to microservices. It continues with some more advanced implications of a microservices architecture, including SLAs, cost-allocation, and vendor-customer relationships within the organization. It concludes by exploring a set of common service anti-patterns.
Concurrency at Scale: Evolution to Micro-ServicesRandy Shoup
Most large-scale web companies have evolved their system architecture from a monolithic application and monolithic database to a set of loosely coupled micro-services. Using examples from Google, eBay, and KIXEYE, this talk outlines the pros and cons of these different stages of evolution, and makes practical suggestions about when and how other organizations should consider migrating to micro-services. It concludes with some more advanced implications of a micro-services architecture, including SLAs, cost-allocation, and vendor-customer relationships within the organization.
QCon New York 2014 - Scalable, Reliable Analytics Infrastructure at KIXEYERandy Shoup
As a maker of real-time strategy games for web and mobile, KIXEYE's business depends on deep insights into how players play our games. By analyzing player behavior in a rich and flexible way, we are able to better target our efforts around user acquisition, game balance, player retention, and game monetization. By storing and analyzing data in standard ways, our data scientists are better able to take learnings from one game and apply them to another.
This presentation describes KIXEYE's newly-minted modern analytics infrastructure soup-to-nuts, from Kafka queues through Hadoop 2 to Hive and Redshift. It outlines our efforts around queryability, extensibility, scalability, standardization, and stability and outage recovery. It further shares our lessons learned in building, testing, operating, and enhancing this mission-critical piece of our infrastructure.
QCon Tokyo 2014 - Virtuous Cycles of Velocity: What I Learned About Going Fas...Randy Shoup
eBay and Google operate some of the largest Internet sites on the planet, and each maintains its leadership through continuous innovation in infrastructure and products. While substantially different in their detailed approaches, both organizations sustain their feature velocity through a combination of organizational culture, process, and people. This session will explore how these large-scale sites do it, and will offer some concrete suggestions on how other organizations -- both large and small -- can do the same.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
6. Technology @randyshoup
• aka “Optimization Function” or “One
Metric That Matters”
• Discussing and agreeing on this metric
is itself valuable
• Only very few metrics, preferably one
Overall Evaluation
Criterion (OEC)
• E.g., Actions vs. click rate
• E.g., Long-term customer value vs.
short-term revenue
• “Pirate metrics” (AARRR): Acquisition,
Activation, Retention, Revenue,
Referral
Aligned to Business
Value
• Validated by data science, not solely
chosen by product / business
• Look for predictive leading indicators
• Avoid lagging indicators and vanity
metrics
Valid and
Measurable
Evaluating Success
Problem
10. Technology @randyshoup
• Many events, only predictive in
aggregate
• E.g., web search queries, ecommerce
clickstream, Netflix viewing metrics
Big but Shallow
• Few events, each of which is significant
• E.g., ecommerce purchases, WeWork
event attendance
Small but Deep
Characterizing Your Data
Data
12. Technology @randyshoup
• Missing data, partial data
• Improperly or inconsistently formatted
Clean Data
• Consolidated into a single (logical)
location so it can be processed or
analyzed
• Joined together (“enriched”) with other
data sources
Aggregated Data
• Tagged by humans with one or more
labels
• Required to train supervised models
• Complicated and expensive at scale
Labeled Data
Better Data
Data
13. Technology @randyshoup
• More potentially useful attributes
• More data sources
• Longer retention
More Data
• Data pipeline to automate collection and
aggregation
• Move from large batch to mini-batch to
streaming data
Timely Data
Better Data
Data
14. “Data preparation accounts
for about 80% of the work of
data scientists.” – CrowdFlower survey,
2016
https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2d58f4ab6f63
16. Technology @randyshoup
• Encode expert knowledge
• Simple set of imperative if-then-else
statements
• Brittle and primitive
• Surprisingly effective
Rules and Heuristics
• Regression
• Decision trees / forests
• Collaborative filtering
• May be all you need
Simple Algorithms
• Iterative Optimization / Dynamic
Programming
• Neural nets
• Deep learning
• Only when absolutely required
Advanced Techniques
Algorithmic Evolution
Algorithms
17. Technology @randyshoup
• Many real-world problems are best
solved through a combination of several
algorithms
• E.g., Netflix Prize
Portfolio / Ensemble
Approaches
Algorithmic Evolution
Algorithms
20. Technology @randyshoup
• Many common algorithms are highly
accurate, but difficult to interpret
• Model can make a decision, but ew
cannot “explain” its decision
• Particularly important in context of
system bias
• (+) Decision trees / forests, linear
regression
• (-) Neural nets, Deep Learning
Interpretability /
Explainability
• Enable data scientists to be self-
sufficient in experimenting, building,
training, and deploying
• End-to-end responsibility for models in
production
• Write models, deploy models, monitor
model performance
DevOps for
Data Science
• Platform-as-a-service for data scientists
• Programming model that matches the
workflow of a data scientist
• Abstract away infrastructure and other
details
Algorithm
Platform
Scaling Algorithm Development
Algorithms
21. Technology @randyshoup
• Data scientists spin up their own resources
• Both ad-hoc execution and repeatable pipelines
• Data science-friendly programming model exposes ETL and
Matrix transforms
• Abstracts away storage (S3), computation (Docker and ECS), and
the model building pipeline (Spark)
Algorithm Platform-as-a-Service
Algorithms
23. “It doesn’t matter how
beautiful your theory is.
It doesn’t matter how
smart you are.
If it doesn’t agree with
experiment, it’s wrong.”
-- Richard Feynman
24. Technology @randyshoup
• What metrics do you expect to move,
and why
• Understand your baseline
1. State Your
Hypothesis
• Sample size based on effect size
• Separate control and treatment groups,
test for bias
• Split traffic between control and
treatment
2. Design a Real A|B
Test
• Understand customer and system
behavior
• Understand why this experiment worked
or did not
3. Obsessively Log and
Measure
Designing and Running
Experimental Discipline
25. Technology @randyshoup
• Data trumps hope and intuition
• Develop insights for the next experiment
4. Listen to the
Data
• This is a journey, not a single step
5. Rinse and Repeat
Designing and Running
Experimental Discipline
26. Technology @randyshoup
Listen to the Data
Experimental Discipline
• 1/3 of ideas were positive and
statistically significant
• 1/3 of ideas were flat: no
statistically significant difference
• 1/3 of ideas were negative and
statistically significant
https://exp-platform.com/experiments-at-microsoft/
27. “Being wrong isn’t a bad
thing, like they teach
you in school. It is an
opportunity to learn
something.”
-- Richard Feynman
28. Technology @randyshoup
• Low-risk, push-button deployment
• Rapid release cadence
• Rapid rollback and recovery
Repeatable Deployment
Pipeline
• Faster to repair
• Easier to understand
• Simpler to diagnose
Smaller Units of Work
• Changes can be rolled out and rolled
back
• Learnings can be applied in the next
experiment
Enables
Experimentation
Continuous Delivery
Experimental Discipline
29. Technology @randyshoup
• Flag controls whether feature is “on” for
a particular set of users
• Independently discovered at eBay,
Yahoo, Google
• Decouple feature delivery from code
delivery
Enable / Disable feature
via configuration
• Develop / test / verify in production
• Rapid on or off for any reason
Makes Speed Safe
• Overall experiment controlled by feature
flag
• Control vs. treatment
Enables
Experimentation
Feature Flags
Experimental Discipline
30. ● Ranking function for search results
○ Small number of hand-tuned factors Thousands of factors
● Incremental Experimentation
○ Predictive models: query->view, view->purchase, etc.
○ Hundreds of parallel A | B tests
○ Full year of steady, incremental improvements
2% increase in eBay revenue (~$120M / year)
@randyshoup
Machine-Learned Ranking
31. ● Reduce user-experienced latency for search results
● Iterative Process
○ Implement a potential improvement
○ Release to the site in an A | B test
○ Monitor metrics –time to first byte, time to click, click rate, purchase rate
2% increase in eBay revenue (~$120M / year)
@randyshoup
Site Speed
36. Technology
Get the predicted
opening occupancy
based on the
recommended 1-Click
price
Adjust the price to see how
occupancy will change
Occupancy Predictor
WeWork Revenue Optimization
@randyshoup
38. Technology
Office Attributes Based Pricing
Corner office (premium)
Offices with high quality
views (premium)
Calculate and recommend
premium and discounts for
key office attributes
WeWork Revenue Optimization
@randyshoup
39. Technology
Example: Recommend alternative usage for unoccupied spaces
Fully optimize inventory usage by
leveraging demand and
profitability predictions
Inventory Management
WeWork Revenue Optimization
@randyshoup
42. Technology @randyshoup
• Identify and frame a clear business
problem
• … that matters to customers or the
business
• Define clear metric(s) for success
1. Drive from Business
Needs
• Single problem
• Solve problem end-to-end
• Show business results
2. Start Small
• Data collection and storage
• Data cleanliness and preparation
• Reliable, accurate, timely data pipeline
• Better data beats a better model (!)
3. Data Matters
Takeaways
An Agile Approach to Machine Learning
43. Technology @randyshoup
• Start with a Hypothesis
• Design an Experiment
• Separate Control and Experiment
group(s)
• Measure business metric for A vs. B
• Learn and Decide
4. A | B Testing
Discipline
• Simple model / No model
• Rules and Heuristics
• Gradually increase sophistication with
more data and more experience
5. Iteratively Refine
Model
• Find broader applicability across the
business
• Apply to more and more problems
• Move “upstream” in the development
process
6. Iteratively Expand
Applications
Takeaways
An Agile Approach to Machine Learning
44. Technology @randyshoup
• Make decisions with data instead of
guesswork and intuition
• Avoid HiPPO decisionmaking
• Can be threatening to designers,
product managers, decisionmakers
7. Data-Driven Culture
• Set of tools in our toolbox
• Sometimes valuable and useful
• Not a panacea
• Not a substitute for thinking
8. Machine Learning is
not Magic
Takeaways
An Agile Approach to Machine Learning