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Discover how to streamline and accelerate machine learning model deployment in this 23-minute conference talk from DSC EUROPE 24. Kevin Dietz explores the challenges of traditional deployment methods that are often slow, manual, and error-prone, while presenting solutions through Continuous Integration (CI) and MLOps frameworks. Learn how CI automates critical tasks including data ingestion, preprocessing, feature engineering, training, validation, code review, and deployment to enable faster iterations and more stable codebases. The presentation demonstrates how MLOps complements CI by managing the entire machine learning lifecycle from creation through deployment and monitoring. This talk was presented on November 21st at DSC EUROPE 24 in Belgrade and offers valuable insights for data scientists looking to improve their model deployment workflows.
