Jordan Edwards: ML Engineering and DevOps on AzureML

Episode 9 of the Machine Learning Street Talk (MLST) podcast, hosted by Machine Learning Street Talk (MLST), titled "Jordan Edwards: ML Engineering and DevOps on AzureML" was published on June 3, 2020 and runs 72 minutes.

June 3, 2020 ·72m · Machine Learning Street Talk (MLST)

Summary

This week we had a super insightful conversation with  Jordan Edwards, Principal Program Manager for the AzureML team!  Jordan is on the coalface of turning machine learning software engineering into a reality for some of Microsoft's largest customers.  ML DevOps is all about increasing the velocity of- and orchastrating the non-interactive phase of- software deployments for ML. We cover ML DevOps and Microsoft Azure ML. We discuss model governance, testing, intepretability, tooling. We cover the age-old discussion of the dichotomy between science and engineering and how you can bridge the gap with ML DevOps. We cover Jordan's maturity model for ML DevOps.  We also cover off some of the exciting ML announcments from the recent Microsoft Build conference i.e. FairLearn, IntepretML, SEAL, WhiteNoise, OpenAI code generation, OpenAI GPT-3.  00:00:04 Introduction to ML DevOps and Microsoft Build ML Announcements 00:10:29 Main show kick-off 00:11:06 Jordan's story 00:14:36 Typical ML DevOps workflow 00:17:38 Tim's articulation of ML DevOps 00:19:31 Intepretability / Fairness 00:24:31 Testing / Robustness 00:28:10 Using GANs to generate testing data 00:30:26 Gratuitous DL? 00:33:46 Challenges of making an ML DevOps framework / IaaS 00:38:48 Cultural battles in ML DevOps 00:43:04 Maturity Model for Ml DevOps 00:49:19 ML: High interest credit card of technical debt paper 00:50:19 ML Engineering at Microsoft 01:01:20 ML Flow 01:03:05 Company-wide governance  01:08:15 What's coming next 01:12:10 Jordan's hillarious piece of advice for his younger self Super happy with how this turned out, this is not one to miss folks!  #deeplearning #machinelearning #devops #mldevops

Episode Description