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Machine Learning Engineering  with Python

You're reading from   Machine Learning Engineering with Python Manage the lifecycle of machine learning models using MLOps with practical examples

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd Edition
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Author (1):
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Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
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Table of Contents (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
11. Index

Working as an effective team

In modern software organizations, there are many different methodologies to organize teams and get them to work effectively together. We will cover some of the project management methodologies that are relevant in Chapter 2, The Machine Learning Development Process, but in the meantime, this section will discuss some important points you should consider if you are ever involved in forming a team, or even if you just work as part of a team, that will help you become an effective teammate or lead.

First, always bear in mind that nobody can do everything. You can find some very talented people out there, but do not ever think one person can do everything you will need to the level you require. This is not just unrealistic; it is bad practice and will negatively impact the quality of your products. Even when you are severely resource-constrained, the key is for your team members to have a laser-like focus to succeed.

Second, blended is best. We all...

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