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You're reading from  Machine Learning Engineering with MLflow

Product typeBook
Published inAug 2021
PublisherPackt
ISBN-139781800560796
Edition1st Edition
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Author (1)
Natu Lauchande
Natu Lauchande
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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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Using the workbench for stock prediction

In this section, we will use the workbench step by step to set up a new project. Follow the instructions step by step to start up your environment and use the workbench for the stock-prediction project.

Important note

It is critical that all packages/libraries listed in the Technical requirements section are correctly installed on your local machine to enable you to follow along.

Starting up your environment

We will move on next to exploring your own development environment, based on the development environment shown in this section. Please execute the following steps:

  1. Copy the contents of the project available in https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/tree/master/Chapter03/gradflow.
  2. Start your local environment by running the following command:
    make
  3. Inspect the created environments, like this:
    $ docker ps 

    The following screenshot presents three Docker images: the first for Jupyter...

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You have been reading a chapter from
Machine Learning Engineering with MLflow
Published in: Aug 2021Publisher: PacktISBN-13: 9781800560796

Author (1)

author image
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