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The MLOps Podcast
Dean Pleban @ DagsHub
35 episodes
4 days ago
A podcast from DagsHub about bringing machine learning into the real world. Each episode features a conversation with top data science and machine learning practitioners, who'll share their thoughts, best practices, and tips for promoting machine learning to production
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Technology
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All content for The MLOps Podcast is the property of Dean Pleban @ DagsHub and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
A podcast from DagsHub about bringing machine learning into the real world. Each episode features a conversation with top data science and machine learning practitioners, who'll share their thoughts, best practices, and tips for promoting machine learning to production
Show more...
Technology
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✍️ Building ML Teams and Platforms with Assaf Pinhasi
The MLOps Podcast
1 hour 18 minutes 11 seconds
2 years ago
✍️ Building ML Teams and Platforms with Assaf Pinhasi

In this episode, I speak with Assaf Pinhasi, ML engineering and MLOps consultant extraordinaire! Assaf was the VP R&D at Zebra Medical Vision, and built the PayPal Risk organization's Big Data Platform. We dive into building ML infrastructure from scratch 10 years ago vs. today, best practices involved in building teams to support machine learning models in production, and the future of generative models.  


Watch the video: https://youtu.be/tSbuDA5tMxQ


Join our Discord community: https://discord.gg/tEYvqxwhah


➡️ Assaf Pinhasi on LinkedIn – https://www.linkedin.com/in/assafpinhasi/ 

🌐 Check Out Our Website! https://dagshub.com   


Social Links:  

➡️ LinkedIn: https://www.linkedin.com/company/dagshub  

➡️ Twitter: https://twitter.com/TheRealDAGsHub  

➡️ Dean Pleban: https://twitter.com/DeanPlbn

The MLOps Podcast
A podcast from DagsHub about bringing machine learning into the real world. Each episode features a conversation with top data science and machine learning practitioners, who'll share their thoughts, best practices, and tips for promoting machine learning to production