Build End-To-End MLOps Platform with Open Source DVC Ecosystem | Mikhail Rozhkov | DSC Europe 23
In this tech tutorial, Mikhail covered the diverse features and use cases of Data Version Control (DVC) in ML engineering and MLOps. Attendees learned to initiate versioning for data, artifacts, and models using DVC and DVCLive, enabling automated pipelines. The tutorial included experiment management and metrics tracking with DVCLive and VSCode, along with setting up CI/CD for testing and deploying ML models using DVC and CML. Mikhail drew on over two years of experience integrating DVC in various ML projects across industries like Banking, Telecommunication, Automotive, Retail, Health, and Public Sector. Participants gained practical insights into the tool's features and limitations for different ML applications, such as computer vision, NLP, and batch scoring.
This tutorial by Mikhail Rozhkov was held on November 21st as part of Tech Tutorials on Stream 3 live at the Data Science Conference Europe 2023.
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