You're reading from Machine Learning Engineering with MLflow

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Published inAug 2021

PublisherPackt

ISBN-139781800560796

Edition1st Edition

Tools

Maven

Concepts

Machine Learning

Author (1)

Natu Lauchande

Developing your machine learning baseline pipeline

For our machine learning platform, we will start with a very simple, heuristic-based pipeline, in order to get the infrastructure of your end-to-end system working correctly and an environment where the machine learning models can iterate on it.

Important note

It is critical that the technical requirements are correctly installed in your local machine to follow along. The assumption on this section is that you have MLflow and Docker installed as per the Technical requirements section.

By the end of this section, you will be able to create our baseline pipeline. The baseline pipeline value is to enable rapid iteration to the model developers. So, basically, an end-to-end infrastructure with placeholders for training and model serving will be made available to the development team. Since it's all implemented in MLflow, it becomes easy to have specialization and focus of the different types of teams involved in a machine...

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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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Developing your machine learning baseline pipeline

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