Abstract
Organizations and developers that are seeking to leverage the power of machine learning (ML) and AI spend a significant amount of time building ML models and are seeking a method for streamlining their machine learning development lifecycle to track experiments, package code into reproducible runs, as well as build, share, and deploy ML models.
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L’Esteve, R.C. (2021). Machine Learning in Databricks. In: The Definitive Guide to Azure Data Engineering. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7182-7_23
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DOI: https://doi.org/10.1007/978-1-4842-7182-7_23
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-7182-7
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