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Machine Learning Engineering  with Python - Second Edition

You're reading from  Machine Learning Engineering with Python - Second Edition

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Pages 462 pages
Edition 2nd Edition
Languages
Author (1):
Andrew P. McMahon Andrew P. McMahon
Profile icon Andrew P. McMahon

Table of Contents (12) Chapters

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process 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

Selecting the tools

Now that we have a high-level design in mind and we have written down some clear technical requirements, we can begin to select the toolset we will use to implement our solution.

One of the most important considerations on this front will be what framework we use for modeling our data and building our forecasting functionality. Given that the problem is a time-series modeling problem with a need for fast retraining and prediction, we can consider the pros and cons of a few options that may fit the bill before proceeding.

The results of this exercise are shown in Table 8.2:

Tool/Framework

Pros

Cons

Scikit-learn

  • Already understood by almost all data scientists.
  • Very easy-to-use syntax.
  • Lots of great community support.
  • ...
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