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

Jordan Edwards: ML Engineering and DevOps on AzureML

Machine Learning Street Talk (MLST)

Jordan Edwards: ML Engineering and DevOps on AzureML