MLOps Coffee Sessions #75 with Shreya Shankar, Towards Observability for ML Pipelines.// AbstractAchieving observability in ML pipelines is a mess right now. We are tracking thousands of means, percentiles, and KL divergences of features and outputs in a haphazard attempt to figure out when and how to retrain models.In this session, we break down current unsuccessful approaches and discuss the path towards effectively maintaining ML models in production. Along the way, we introduce mltrace -- a preliminary open source project striving towards "bolt-on" observability in ML pipelines.// BioShreya Shankar is a computer scientist living in the Bay Area. She's interested in building systems to operationalize machine learning workflows. Shreya's research focus is on end-to-end observability for ML systems, particularly in the context of heterogeneous stacks of tools.Currently, Shreya is doing her Ph.D. in the RISE lab at UC Berkeley. Previously, she was the first ML engineer at Viaduct, did research at Google Brain, and completed her BS and MS in computer science at Stanford University.// Related Links Shreya Shankar's blogposts: https://www.shreya-shankar.com/ Shreya Shankar's Podcasts: https://www.listennotes.com/top-episodes/shreya-shankar/ The deployment phase of machine learning by Benedict Evans: https://www.ben-evans.com/benedictevans/2019/10/4/machine-learning-deployment Shreya Shrankar's mltrace blogpost: https://www.shreya-shankar.com/introducing-mltrace/--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/Connect with Shreya on LinkedIn: https://www.linkedin.com/in/shrshnkTimestamps: [[[[[[[[[[[[[[[[[[[[[[[[[[[[[[