Developer Tea exists to help driven developers connect to their ultimate purpose and excel at their work so that they can positively impact the people they influence. With over 17 million downloads to date, Developer Tea is a short podcast hosted by Jonathan Cutrell, engineering leader with over 15 years of industry experience. We hope you'll take the topics from this podcast and continue the conversation, either online or in person with your peers. Email: [email protected]
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محتوای ارائه شده توسط Charles M Wood. تمام محتوای پادکست شامل قسمتها، گرافیکها و توضیحات پادکست مستقیماً توسط Charles M Wood یا شریک پلتفرم پادکست آنها آپلود و ارائه میشوند. اگر فکر میکنید شخصی بدون اجازه شما از اثر دارای حق نسخهبرداری شما استفاده میکند، میتوانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Unraveling the Complexities of Model Deployment in Dynamic Marketplaces - ML 151
Manage episode 417673777 series 2977446
محتوای ارائه شده توسط Charles M Wood. تمام محتوای پادکست شامل قسمتها، گرافیکها و توضیحات پادکست مستقیماً توسط Charles M Wood یا شریک پلتفرم پادکست آنها آپلود و ارائه میشوند. اگر فکر میکنید شخصی بدون اجازه شما از اثر دارای حق نسخهبرداری شما استفاده میکند، میتوانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Deeksha Goyal is the Senior Machine Learning Engineer at Lyft and Michael Sun is the Staff Software Engineer at Lyft. They delve into the intricacies of machine learning and data-driven technology. In this episode, they explore the challenges and innovations in deploying models into production, particularly focusing on the real-world implications of ETA (Estimated Time of Arrival) modeling at Lyft. They share valuable insights, from the complexities of A/B testing and long-term impact assessment, to the dynamic nature of handling real-time data and addressing unpredictability in route predictions. Join them as they journey through the world of model deployment, bug identification, and career development within the fast-paced environment of Lyft's data-driven infrastructure.
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Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
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Sponsors
Socials
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
209 قسمت
Manage episode 417673777 series 2977446
محتوای ارائه شده توسط Charles M Wood. تمام محتوای پادکست شامل قسمتها، گرافیکها و توضیحات پادکست مستقیماً توسط Charles M Wood یا شریک پلتفرم پادکست آنها آپلود و ارائه میشوند. اگر فکر میکنید شخصی بدون اجازه شما از اثر دارای حق نسخهبرداری شما استفاده میکند، میتوانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Deeksha Goyal is the Senior Machine Learning Engineer at Lyft and Michael Sun is the Staff Software Engineer at Lyft. They delve into the intricacies of machine learning and data-driven technology. In this episode, they explore the challenges and innovations in deploying models into production, particularly focusing on the real-world implications of ETA (Estimated Time of Arrival) modeling at Lyft. They share valuable insights, from the complexities of A/B testing and long-term impact assessment, to the dynamic nature of handling real-time data and addressing unpredictability in route predictions. Join them as they journey through the world of model deployment, bug identification, and career development within the fast-paced environment of Lyft's data-driven infrastructure.
Sponsors
Socials
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
…
continue reading
Sponsors
Socials
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
209 قسمت
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