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Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

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


Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

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

ratings:
Length:
39 minutes
Released:
Dec 2, 2019
Format:
Podcast episode

Description

Today we’re joined by Jordan Edwards, Principal Program Manager for MLOps on Azure ML at Microsoft. In our conversation, Jordan details: How Azure ML accelerates model lifecycle management with MLOps, enabling data scientists to collaborate with IT teams to increase the pace of model development and deployment. Problems associated with generalizing ML at scale at Microsoft, and how those problems are prioritized,  What is MLOps, and the role of testing is in an MLOps environment, and experiences working with customers to implement these tests.  The “four phases” along the journey of customer implementation of MLOps, how companies should look at hiring ML Engineers vs DevOps Engineers, and other aspects of managing model life cycles that Jordan finds important for us to think about.  The complete show notes can be found at twimlai.com/talk/321. 
Released:
Dec 2, 2019
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.