You're reading from Machine Learning Engineering with MLflow

Product typeBook

Published inAug 2021

PublisherPackt

ISBN-139781800560796

Edition1st Edition

Tools

Maven

Concepts

Machine Learning

Author (1)

Natu Lauchande

Technical requirements

For this chapter, you will need the following prerequisites:

The latest version of Docker installed on your machine. If you don't already have it installed, please follow the instructions at https://docs.docker.com/get-docker/.
The latest version of docker-compose installed. Please follow the instructions at https://docs.docker.com/compose/install/.
Access to Git on the command line and installed as described at https://git-scm.com/book/en/v2/Getting-Started-Installing-Git.
Access to a Bash terminal (Linux or Windows).
Access to a browser.
Python 3.5+ installed.
The latest version of your machine learning installed locally as described in Chapter 3, Your Data Science Workbench.

In the next section, we will describe the structure of our data pipeline, the data sources, and the different steps that we will execute to implement our practical example leveraging MLflow project features...

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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.
Read more about Natu Lauchande

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