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Kelley Rivoire

9 Podcast Episodes

Latest 18 Sep 2021 | Updated Daily

Weekly hand curated podcast episodes for learning

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Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat)

Software Engineering Daily

Originally published June 13, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production. An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model. Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company. Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard. The post Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat) appeared first on Software Engineering Daily.

1hr 4mins

16 Jun 2020

Episode artwork

Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat)

Machine Learning – Software Engineering Daily

Originally published June 13, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production. An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model. Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company. Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard. The post Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat) appeared first on Software Engineering Daily.

1hr 4mins

16 Jun 2020

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Episode artwork

Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat)

Podcast – Software Engineering Daily

Originally published June 13, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production. An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model. Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company. Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard. The post Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat) appeared first on Software Engineering Daily.

1hr 4mins

16 Jun 2020

Episode artwork

Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire (Summer Break Repeat)

Software Daily

Originally published June 13, 2019. We are taking a few weeks off. We’ll be back soon with new episodes.Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization.There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production.An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model.Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company.Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard.

16 Jun 2020

Most Popular

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Machine Learning Systems (Kelley Rivoire)

The Women in Tech Show: A Technical Podcast

Learning from data is something that is being used to improve processes across different industries. Kelley Rivoire, Engineering Manager at Stripe, explained how machine learning is used for payment processing applications. We talked about what machine learning is, and about Stripe’s real-time machine learning based system to evaluate user risk. Kelley also explained the process of deploying machine learning systems to production and the challenges.

20 Aug 2019

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Enjoy the Present Kelley Rivoire

5 Minute Mentor

Subscribe on: iTunes | Spotify | Android | YouTubeKelley Rivoire is an Engineering Manager at Stripe, an American technology company valued at 22 billion in 2019. Kelley worked on building the company’s first real-time Machine Learning model for evaluation of user risk. Prior to that, Kelley studied Physics at MIT and a PhD in Electrical Engineering at Stanford University.https://media.blubrry.com/5minutementor/5minutementor473546891.files.wordpress.com/2019/08/5-kelley-rivoire-music.mp3Transcript:ES: This is 5 Minute Mentor, a podcast where you’ll get advice from prominent engineers, authors, artists, and more in 5 minutes or less.KR: Hi, I’m Kelley Rivoire I’m an Engineering Manager at Stripe, and I’d like to tell you a little bit about advice that I found helpful in my career. KR: The guiding principle I’ve probably used the most is to make sure that I’m fully committing myself to what I’m doing, but also not over optimizing too much for the future, and really enjoying what I’m doing right now. I think a few times, I found that it’s tempting to kind of think about where you want to go and what you need to get there, and start thinking about spending time on that, but realize that you don’t actually enjoy that thing. So for example, when I was in grad school, I worked on nanophotonics and I realized that material science was really important, and I thought maybe I’ll go do a postdoc in that. Then I realized that I actually really didn’t like working on that problem. And so it made sense to kind of do something different and focus on what I really did enjoy.KR: So I think that’s something that’s carried me through pretty well, in my career, to go after what I’m doing and do my best at it, but also make sure that I enjoy what I’m doing right now, and I’m not doing something that I hate, in pursuit of something that maybe I’ll like better 5 years from now. So I encourage everyone to do the same.Kelley Rivoire, Engineering Manager at Stripe

7 Aug 2019

Episode artwork

Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire

Machine Learning – Software Engineering Daily

Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production. An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model. Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company. Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard. ANNOUNCEMENTS FindCollabs is a place to find collaborators and build projects. FindCollabs is the company I am building, and we are having an online hackathon with $2500 in prizes. If you are working on a project, or you are looking for other programmers to build a project or start a company with, check out FindCollabs. I’ve been interviewing people from some of these projects on the FindCollabs podcast, so if you want to learn more about the community you can hear that podcast. New Software Daily app for iOS. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe Upcoming conferences I’m attending: Datadog Dash July 16th and 17th in NYC, Open Core Summit September 19th and 20th in San Francisco We are hiring two interns for software engineering and business development! If you are interested in either position, send an email with your resume to jeff@softwareengineeringdaily.com with “Internship” in the subject line. The post Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire appeared first on Software Engineering Daily.

1hr 3mins

13 Jun 2019

Episode artwork

Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire

Software Daily

Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization.There are many steps that an engineer must go through to use machine learning, and each additional step inhibits the chances that the engineer will actually get their model into production.An engineer who wants to build machine learning into their application needs access to data sets. They need to join those data sets, and load them into a machine (or multiple machines) where their model can be trained. Once the model is trained, the model needs to test on additional data to ensure quality. If the initial model quality is insufficient, the engineer might need to tweak the training parameters. Once a model is accurate enough, the engineer needs to deploy that model. After deployment, the model might need to be updated with new data later on. If the model is processing sensitive or financially relevant data, a provenance process might be necessary to allow for an audit trail of decisions that have been made by the model.Rob Story and Kelley Rivoire are engineers working on machine learning infrastructure at Stripe. After recognizing the difficulties that engineers faced in creating and deploying machine learning models, Stripe engineers built out Railyard, an API for machine learning workloads within the company.Rob and Kelley join the show to discuss data engineering and machine learning at Stripe, and their work on Railyard.

13 Jun 2019

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Scaling Model Training with Kubernetes at Stripe with Kelley Rivoire - TWIML Talk #272

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Kelley Rivoire, engineering manager working on machine learning infrastructure at Stripe. Kelley and I caught up at a recent Strata Data conference to discuss:• Her talk "Scaling model training: From flexible training APIs to resource management with Kubernetes."• Stripe’s machine learning infrastructure journey, including their start from a production focus.• Internal tools used at Stripe, including Railyard, an API built to manage model training at scale & more!

42mins

6 Jun 2019