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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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Solving the Real Issues with the MLflow Team - ML 059

48:35
 
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Manage episode 318781160 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
If you’re looking for a team that actually cares about the issues you’re facing, look no further than Databricks, and they’ve got something exciting out. In this episode, Michael and Ben welcome on the development team of MLflow, an open-source lifecycle manager for machine learning. They cover how Databricks is redefining how developers and engineers collaborate, the reason behind Databricks’ crazy success, and the number ONE most important testing structure for any development team. “A lot of the success was attributed to process and dedicated focus on the interface, understanding what major problems we were going after. ”
- Corey Zumar In This Episode How Databricks allows data analysis, engineers, and developers to collaborate effectively
Why Databricks was able to rake in 800,000 downloads per MONTH in their first year
A simple but powerful methodology that helps Databrick identify the highest ROI problems to tackle (not just the most popular ones)
The number one MOST important testing structure that reveals how Databricks keeps their work top-notch
What makes Databricks unique from everyone else and is the KEY to putting users first in 2022 Sponsors Special Guests: Corey Zumar, Harutaka Kawamura, Weichen Xu, and Zhang Jin.Sponsored By:
Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
  continue reading

209 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 318781160 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
If you’re looking for a team that actually cares about the issues you’re facing, look no further than Databricks, and they’ve got something exciting out. In this episode, Michael and Ben welcome on the development team of MLflow, an open-source lifecycle manager for machine learning. They cover how Databricks is redefining how developers and engineers collaborate, the reason behind Databricks’ crazy success, and the number ONE most important testing structure for any development team. “A lot of the success was attributed to process and dedicated focus on the interface, understanding what major problems we were going after. ”
- Corey Zumar In This Episode How Databricks allows data analysis, engineers, and developers to collaborate effectively
Why Databricks was able to rake in 800,000 downloads per MONTH in their first year
A simple but powerful methodology that helps Databrick identify the highest ROI problems to tackle (not just the most popular ones)
The number one MOST important testing structure that reveals how Databricks keeps their work top-notch
What makes Databricks unique from everyone else and is the KEY to putting users first in 2022 Sponsors Special Guests: Corey Zumar, Harutaka Kawamura, Weichen Xu, and Zhang Jin.Sponsored By:
Advertising Inquiries: https://redcircle.com/brands
Privacy & Opt-Out: https://redcircle.com/privacy
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
  continue reading

209 قسمت

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