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Vishwajyoti Pandey - Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps

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Vishwajyoti Pandey Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps
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Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps: summary, description and annotation

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Table of Contents
Guide

Operationalizing Machine Learning Pipelines - photo 1

Operationalizing
Machine Learning
Pipelines

Building Reusable and Reproducible Machine Learning Pipelines Using MLOps - photo 2

Building Reusable and Reproducible
Machine Learning Pipelines Using MLOps

Operationalizing Machine Learning Pipelines Building Reusable and Reproducible Machine Learning Pipelines Using MLOps - image 3

Vishwajyoti Pandey
Shaleen Bengani
Operationalizing Machine Learning Pipelines Building Reusable and Reproducible Machine Learning Pipelines Using MLOps - image 4

www.bpbonline.com

FIRST EDITION 2022

Copyright BPB Publications, India

ISBN: 978-93-55510-235

All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means.

LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY

The information contained in this book is true to correct and the best of authors and publishers knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book.

All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.

wwwbpbonlinecom Foreword We reap what we sow We are the makers of our - photo 5

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Foreword

We reap what we sow. We are the makers of our own fate.
None else has the blame, none has the praise.
Swami Vivekananda

The learning is a never-ending process. This is a 360o circle by which you can rehearse and enhance your skills. This is a process to learn -> implement -> analyze -> fix -> and so on. Sometime, we can also call 7-colors of life (not RAINBOW). All these colors attract us to achieve our dreams. This book gives you a push towards one step ahead to achieve your dreams.

This is our brain, which tells the meaning of juggle (here refer to above para). In general, the possibility of following two types of brain:

  • first, can assess the meaning of the words similar to above para,
  • second, cant assess or find it difficult to assess

If you are of first type have to read this book and if of second type then must to read this book. Now, start reading this page from its first line until first stanza complete. There are chances that you repeat the reading twice, thrice or more time but in every cycle of reading you will find that youre getting the actual meaning of the juggle. Once youre doing this, in every read cycle, your brain is self-preparing, so, that when you try to recall the meaning then your brain will tell you. This is what we called Train-the-model. Here model can be any object (in our case, it is brain). This is what the machine learning refers to. I am sure, youre smiling and thinking is machine learning so easy. Yes, it is the beauty of this book and the hard-work of the author. Author drafted and presented the contents in such a way that youll adapt the technique very easily, just like biting a bread. I started this foreword with the quotes of Swami Vivekanand and this book expounds the quote. Do adhere the rules explained by the author and be the creator of your fate.

The author has built the basic blocks of the widely used topics in detailed manner, and are:

  • Initial setup Identifying the team, priorities the business problems and so on.
  • ML Life cycle.
  • ML Architecture.
  • Training ML models, Building ML pipeline.
  • Deployment & consumption, Operational Aspect.

This book is built with basic blocks and can be a ready reckoner for any professional or student. The book has code-examples and can be found in the repository as mentioned thereon for specific chapters.

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