Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques. MLflow and the model registry can act as powerful tools to simply building a robust CI/CD pattern for any given model In this talk we will explore how MLflow- specifically the model registry – can be integrated with continuous integration, continuous development, and continuous deployment tools. We’ll walk though an end to end example of designing a CI/CD process for a model deployment and implementing with MLflow and automation tools
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Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
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LinkedIn: [ Ссылка ]: [ Ссылка ] Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. [ Ссылка ]
