DevOps in Data Science - What Works and What Doesn't

Explore the challenges and solutions in implementing DevOps practices for data science projects in this 22-minute conference talk from KubeCon + CloudNativeCon Europe 2023. Discover why balancing the needs of ML engineers and data scientists can lead to tension and technical debt. Learn from the speakers' experience in leveraging Kubeflow, the leading open-source MLOps platform, to empower data scientists with self-service container building using Kaniko and Kubeflow Pipelines. Understand the reasons behind their initial failure and subsequent "DevOps detox." Gain insights into why Kserve emerged as a more suitable, lightweight, and production-ready alternative for improving outcomes in ML model deployment. Reflect on the lessons learned and how engineers can contribute to enhancing the open-source MLOps community.

DevOps in Data Science: What Works and What Doesn’t - Chase Christensen & Stefano Fioravanzo