You're reading from Machine Learning Engineering with MLflow

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Published inAug 2021

PublisherPackt

ISBN-139781800560796

Edition1st Edition

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Maven

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Machine Learning

Author (1)

Natu Lauchande

Creating your own data science workbench

In order to address common frictions for developing models in data science, as described in the previous section, we need to provide data scientists and practitioners with a standardized environment in which they can develop and manage their work. A data science workbench should allow you to quick-start a project, and the availability of an environment with a set of starting tools and frameworks allows data scientists to rapidly jump-start a project.

The data scientist and machine learning practitioner are at the center of the workbench: they should have a reliable platform that allows them to develop and add value to the organization, with their models at their fingertips.

The following diagram depicts the core features of a data science workbench:

Figure 3.1 – Core features of a data science workbench

In order to think about the design of our data science workbench and based on the diagram in Figure...

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Machine Learning Engineering with MLflow

Published in: Aug 2021Publisher: PacktISBN-13: 9781800560796

Author (1)

Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
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