Episode
AI Show | High Level MLOps with Microsoft Data Scientists | Episode 33
On this episode of the AI Show, we're talking about MLOps. Seth welcomes Microsoft Data Scientist, Spyros Marketos, ML Engineer, Davide Fornelli and Data Engineer, Samarendra Panda. Together they make up an AI Taskforce and they'll give us a high-level intro into MLOps and share some of the surprises and lessons they've learned along the way!
Jump to:
[00:17] AI Show Intro
[00:34] Welcome and Introductions
[01:41] Use cases from the AI Taskforce
[02:47] Commonalities across projects
[03:50] Common challenges - from the Data Engineer perspective
[06:47] Common challenges - from the ML Engineer perspective
[08:46] Common challenges from the Data Science perspective
[10:48] What does success in MLOps look like?
[12:30] Surprising challenges working with customers and how to avoid them
[19:27] Review - what is ML Ops
[19:45] MLOps in Delivery mission
[21:57] MLOps principles
[27:52] Tips from the pros
Learn more:
Machine Learning for Data Scientists
Pakt: Principles of Data Science
Zero to Hero Machine Learning on Azure
Zero to Hero Azure AI
Create a Free account (Azure)
Follow Seth on Twitter
Follow Spyros on LinkedIn
Follow Davide on LinkedIn
Follow Sam on LinkedIn
Don't miss new episodes, subscribe to the AI Show and AI Show Playlist
Join us every other Friday, for an AI Show livestream on Learn TV and YouTube.
On this episode of the AI Show, we're talking about MLOps. Seth welcomes Microsoft Data Scientist, Spyros Marketos, ML Engineer, Davide Fornelli and Data Engineer, Samarendra Panda. Together they make up an AI Taskforce and they'll give us a high-level intro into MLOps and share some of the surprises and lessons they've learned along the way!
Jump to:
[00:17] AI Show Intro
[00:34] Welcome and Introductions
[01:41] Use cases from the AI Taskforce
[02:47] Commonalities across projects
[03:50] Common challenges - from the Data Engineer perspective
[06:47] Common challenges - from the ML Engineer perspective
[08:46] Common challenges from the Data Science perspective
[10:48] What does success in MLOps look like?
[12:30] Surprising challenges working with customers and how to avoid them
[19:27] Review - what is ML Ops
[19:45] MLOps in Delivery mission
[21:57] MLOps principles
[27:52] Tips from the pros
Learn more:
Machine Learning for Data Scientists
Pakt: Principles of Data Science
Zero to Hero Machine Learning on Azure
Zero to Hero Azure AI
Create a Free account (Azure)
Follow Seth on Twitter
Follow Spyros on LinkedIn
Follow Davide on LinkedIn
Follow Sam on LinkedIn
Don't miss new episodes, subscribe to the AI Show and AI Show Playlist
Join us every other Friday, for an AI Show livestream on Learn TV and YouTube.
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