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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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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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Introducing the stock market prediction problem

The scenario that we will cover in the remaining chapters of the book is of the hypothetical company PsyStock LLC, which provides a platform for amateur traders, providing APIs and UIs to solve different predictions in the context of stock prediction.

As machine learning practitioners and developers, we should be able to build a platform that will allow a team of data scientists to quickly develop, test, and bring into production machine learning projects.

We will apply and frame the problems initially so we can build our platform upon the basis of the definitions of the problems. It should be noted that the problem framing will evolve as we learn more about the problem: the initial framing will give us guidance on the problem spaces that we will be tackling.

The following are the core projects that we will use as references in the rest of the book for machine learning development in MLflow.

Stock movement predictor

This...

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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