Systems Engineer Navigating the World of ML // Andrew Dye // MLOps Podcast #136

MLOps Coffee Sessions #136 with Andrew Dye, Systems Engineer Navigating the World of ML co-hosted by David Aponte. // Abstract We don't hear that much about working at a very low level on this podcast but they are still very valid. Andrew is able to give us his take on why and what you need to keep in mind when you are working at these low levels and why it is very important when you are a Machine Learning Engineer and how the two can play together nicely. Most MLOps teams are formed using existing people and exitsing engineers. More often than not you have to blend these various disciplines and it works well when there's a common goal. // Bio Andrew is a software engineer at Union and contributor to Flyte, a production grade data and ML orchestration platform. Prior to that he was a tech lead for ML Infrastructure at Meta, where he focused on ML training reliability. // MLOps Jobs board   https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrewwdye Timestamps: [00:00] Andrew's preferred coffee [03:30] Introduction to Andrew Dye [03:33] Takeaways [07:32] Huge shoutout to our sponsors UnionML and UnionAI! [07:48] Andrew's background [10:08] Andrew's learning curve [11:10] Bridging the gap between firmware space and MLOps [12:18] In connection with Pytorch team [12:54] Things that should have learned sooner [14:54] Type of scale Andrew works on [17:42] Distributed training at Meta [19:55] Managing the huge search space [22:18] Execution patterns programs [23:20] Non-ML engineers dealing with ML engineers having the same skill set [26:44] Pace rapid change adoptation [29:18] Consensus challenges [32:26] Abstractions making sense now [34:53] Comparing to others [39:21] General principles in UnionAI tooling [41:54] Seeing the future [43:54] Inter-task checkpointing [44:52] Combining functionality with use cases [46:17] Wrap up