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
