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Machine Learning Engineering with Python

Machine Learning Engineering with Python

By : Andrew P. McMahon
4.9 (21)
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Machine Learning Engineering with Python

Machine Learning Engineering with Python

4.9 (21)
By: Andrew P. McMahon

Overview of this book

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.
Table of Contents (13 chapters)
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1
Section 1: What Is ML Engineering?
4
Section 2: ML Development and Deployment
9
Section 3: End-to-End Examples

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 Figure 7.3:

Figure 7.3 – The considered pros and cons of some different ML toolkits for solving this forecasting problem

Based on the information in Figure 7.3, it looks like the Prophet library would be a good choice and offer a nice balance between predictive power, desired time series capabilities, and experience among the developers and scientists on the team.

...
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Machine Learning Engineering with Python
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