La standardizzazione nel ciclo di vita del Machine Learning

Find out how to successfully build, deploy and reproduce ML models at scale. And learn engineering best practices, discover why MLflow has emerged as a leader in automating the end-to-end ML lifecycle with over 2 million monthly downloads and get an introduction to MLflow’s newest component — Model Registry. The eBook covers:

  • Key challenges organizations face when managing ML models throughout their lifecycle
  • How MLflow addresses these challenges, including experiment tracking, project reproducibility, and model deployment and management
  • How Managed MLflow provides a fully managed, integrated experience with enterprise reliability, security and scale on Databricks

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